Superhuman built its reputation on a number: 100 milliseconds. Every interaction in the product has to feel instantaneous. Not fast. Instantaneous. That’s the threshold where the human brain stops perceiving lag and starts feeling like the software is an extension of thought. They’ve been engineering to that constraint for years, and it has shaped everything — the architecture, the hiring bar, the way even a billing email gets crafted like a product.
Then they added AI. And for the first time, they were shipping something they couldn’t fully control.
The feeling that built a company
“Every single interaction needs to be below 100 milliseconds, because this is when you feel that things are instantaneous,” Loic says. The number didn’t come from a product spec. It came from game design. Rahul Vohra, Superhuman’s CEO, studied how games create the feeling of flow, and bet that people hate email because of how email works, not because email is email.
The architecture follows from that constraint. Superhuman assumes the network will slow you down, so they build as if the network isn’t there — local-first, syncing in the background, optimistic UI throughout. “You need to build without a backend. How do you do that across multiple devices and make it crazy fast?”
People pay $40 a month for email and feel it’s worth it. Their users — mostly executives and salespeople who average three hours a day in their inboxes — describe the experience the way people describe good tools: the software stops mattering and the work takes over.
How taste becomes infrastructure
Loic joined at the beginning of 2025 as an outsider. “I came in with genuine curiosity. I was blown away.” What surprised him wasn’t the rule but how thoroughly it had been internalized. “Even a backend engineer will think about the latency of their API and how this will reflect in the experience.” In most engineering organizations, backend engineers think about correctness and throughput. At Superhuman, they think about how the user will feel.
It starts in hiring — product sense is a criterion for every role, not just product and design. The finance team applies the same scrutiny to the email a customer gets when they’re being told what they owe as the product team applies to the inbox. The offer letter is a product experience. “The offer is a ceremony. It’s not transactional — it’s already an experience.” Candidates who got that treatment show up acting like it.
Rahul reviews everything going into production. “Within the organization, this is building a muscle in every single engineer, designer, product manager — everyone knows the bond is that high.” You can’t work at Superhuman long without developing an eye for when something feels off — a slightly slow animation, a misaligned pixel, an API call that’s a few milliseconds slower than it ought to be.
Loic calls it sensation transference. Packaging changes how you experience the product inside. They take that idea seriously enough that the bill you get from the finance team is treated like part of the product.
The part they can’t control
For ten years, everything in Superhuman’s stack was deterministic. Same input, same output. That’s what made the 100ms promise keepable: you could engineer to it, measure it, hold it.
AI broke that.
“The consistency we were used to is not there anymore,” Loic says. “We all face the surprising change of behavior of a model that is technically not changing its version.” A model API doesn’t update its version number, but its outputs shift. The same query returns different results this week than last week.
For most products, this is annoying. For Superhuman, it’s a more serious problem, because their users aren’t tolerant of inconsistency. “We are similar to Apple in the sense that people expect the best. They pay a bunch, so they always expect the best.”
The specific problem is what happens when AI meets user-generated input. Superhuman can engineer every designed interaction. They cannot engineer how users phrase search queries. “We were controlling every single part of the interaction — feels fast, feels right, feels correct — and all of a sudden, the outcome of the search box is not what I was looking for. Garbage in, garbage out. But how do you control the garbage in?”
There’s no bug to fix and no perf target to chase. The product was built on consistency, and now consistency is the thing they can’t fully promise.
What the numbers don’t say
Superhuman’s AI adoption numbers look good: 90% of engineers using AI daily, 70% of PRs AI-augmented, 90% of those interactions net positive, some engineers claiming 40% velocity gains.
Loic is careful about how he explains this. The numbers work partly because of who their engineers are. “We have a very senior team — over-optimized on seniority. Those people tend to use AI with care. They know the outcome they want, and they just use AI to get faster to that outcome.”
The 40% gains aren’t coming from code generation. They’re coming from everything before the code. “Coming into a new codebase, trying to understand what this library is doing — before, you had to find the entry point, map the dependencies, build your own mental model. Now Claude Code does that so much faster.” The win is in comprehension and orientation, not typing speed.
But the same playbook doesn’t transfer automatically. “If you have a lot of junior engineers, vibe coding’s impact on code quality might be real. It’s not a problem for us — it’s not part of our DNA.” Taste filters the output. Senior engineers with strong judgment about what “right” looks like can catch what the model gets wrong. Engineers without that judgment can’t.
Teams celebrating big AI velocity gains may be doing so because they have enough experienced judgment to catch the mistakes. Teams where most of the engineers are still building that judgment may be accumulating comprehension debt they don’t know about yet.
The acquisition test
The Grammarly acquisition tests the same question at a different scale: can Superhuman’s taste survive contact with mass distribution?
Grammarly has the opposite profile. They’re embedded in Google Docs, Word, email clients, browsers. They have AI capabilities built over years of NLP work. What they’ve optimized for is breadth: supporting every kind of user, every context. Superhuman has been doing the opposite, going deep on one persona and refusing to compromise.
Loic frames the challenge clearly: “How do we make Superhuman not this niche, very fancy application, but something brought to the mass — while keeping our identity?” He reaches for Apple as the reference point. “Learning from Grammarly’s scale and AI capabilities, keeping our culture and taste, and bringing that to the mass — that would be really interesting.”
It’s a genuinely hard problem. Making things simple is hard. Linear built something delightful for small engineering teams, then got successful, then came the bigger companies, the feature requests, the complexity. The focus that made it work is what success makes hardest to maintain.
What this means for you
Superhuman is hitting a wall any product with a quality bar will hit. Three things their experience suggests are worth borrowing.
Make your implicit promises explicit. Superhuman’s was 100ms and determinism — they had ten years of architecture built around it before AI made determinism optional. Most teams have a similar promise they’ve never said out loud: accuracy, consistency, availability, something. Find yours before the model finds it for you, because you can’t defend a contract you haven’t named.
Treat the prompt box as a UX surface, not a backend problem. The moment that surprised Loic wasn’t a model bug — it was the search box. Users phrase queries badly. Prompts are now part of the interface the user sees, and “garbage in, garbage out” is no longer an engineering excuse. Better prompts and evals matter, but if the search box returns the wrong thing, the design team owns that, not the ML team.
Don’t credit the tools for what your senior engineers are doing. Superhuman’s 40% velocity gains work because the people using AI know what right looks like and catch what the model gets wrong. If your team is junior, the same playbook will produce comprehension debt instead of speed. Once you can’t tell the tool’s contribution from the engineer’s, you’re not measuring AI productivity. You’re measuring how much taste you happened to hire.
Loic spent time before tech in contexts where craft standards weren’t optional and the feedback was immediate — a French Navy vessel that had to be back at sea in six weeks, no extensions. The discipline from that kind of constraint is different from the kind you get from a style guide. You learn it because you have no choice, and then it doesn’t really leave. He thinks that’s what Superhuman has built. He’s been there less than a year. Whether the taste travels at Grammarly scale is the thing he’s actually being paid to find out.
High Output is brought to you by Maestro AI. Loic’s AI numbers look good — 90% daily adoption, 40% velocity gains — but he’s the first to say the metrics don’t explain themselves. They work because his senior engineers have the judgment to catch what the model gets wrong. Most engineering leaders have no way to see that layer. You can see PR counts and cycle time. You can’t see whether your engineers are using AI well or just generating output faster. Maestro’s daily briefings reveal where your team’s time and energy actually go — not just what shipped, but the quality of the judgment behind it.
Visit https://getmaestro.ai to see how we help engineering leaders understand what their AI adoption numbers actually mean.
