An AI email client is Harsha Gaddipati’s third product in two years. The first two both hit ten thousand users in their opening week, and both failed anyway. In Episode 6 of the NoRobots Podcast, Vlad asks him what went wrong twice, why email turned out to be the right thing to own, and what people actually automate once they have the tools.
Ten thousand users, twice, and still no business
The first product was a website builder, launched right as Lovable was taking off. Social posts went viral, ten million views across platforms, ten thousand users in week one. You would assume that is a company.
It wasn’t, because the use cases were one-offs: a resume, a business site, something for a partner’s anniversary. People finished the task and never converted off the free trial. The lesson was that you need to own a workflow that repeats.
So they built a general AI agent. Same result on signups, same problem underneath, but with a sharper edge. Power users loved it and described it as the thing they reach for when ChatGPT or Claude fails. Harsha is blunt about what that means: if your market is what the big models can’t do, your addressable market shrinks every week, and you eventually die not because the product is bad but because that entry point is already won.
Why email
His framing for the third attempt: there are only about six places the work you want to hand off actually arrives. Email, messages, Slack, your to-do list, meetings you take in person, and your own anxieties.
Of those, email is the easiest to disrupt, because it lacks the network effects the others have. Slack needs the whole organisation to move. iMessage needs everyone you talk to. Email can be adopted one person at a time.
The trade-off is honest: getting someone onto a new email client is harder than getting them to try a chat tool. But losing them afterwards is also much harder. His summary of the priority — when you’re trying to build something enormous, retention matters more than acquisition.
The forty-hour sessions
Reports of forty-hour building stretches are accurate, but the framing is not what people assume. It’s never a decision to work forty hours. It’s a milestone: first users tomorrow, so what has to be true by then.
The constraints — sending works reliably, labelling works — are usually met in the first six to eight hours, especially with AI doing the typing. The remaining time goes into walking the user flows and fixing the parts that don’t feel right, which is a different bar than passing a test. The two founders swap: one writes a feature, the other tests it.
His reasoning for why that matters: email is a workflow, and in consumer products the user has to feel some joy getting through it, not merely complete the task.
Selling against a switching cost
Nobody pays for an email client if their life isn’t busy, so the only prospects worth talking to are the people with the least time to switch.
The pitch that works isn’t “your current process is broken” — a successful person reasonably believes their process is fine. It’s an outcome question: do you feel you have enough time in the day, and are you hitting your goals? If not, and nothing else is going to change, why not spend thirty minutes on setup for the chance of saving an hour a day.
Beyond that, it’s word of mouth, which he considers the number one way anyone tries anything, plus workshops that put the product in front of people who already trust someone using it.
How the drafting learns
Most tools treat drafting as an isolated action. Slashy treats it as one action in the ongoing life of an agent that starts learning the moment you sign up, which is why the output drifts closer to your voice over time.
What feeds a draft: your past emails, a lookup of who the recipient is and what their site says, your previous emails with a similar goal — a contract negotiation pulls your other contract negotiations — plus your calendar for anything involving times. And crucially, the diffs between what it drafted before and what you actually sent. Models carry their own biases; without seeing the correction, they never converge on you. On top of that sits a memory layer indexing facts about the user, down to food preferences.
Flat pricing, on purpose
Pricing moved from credits to a flat thirty dollars a month, and the reasoning is a bet about where inference costs are heading.
If you charge per AI call and the cost of intelligence trends toward zero, you have built a business that shrinks as the technology improves. He points at coding tools as the illustration: a user switching from an expensive model to a cheap one takes most of that revenue with them. For a company aiming at real scale, he considers pure usage-based pricing a poor long-term structure.
The features nobody expected, and one nobody used
Two surprises came from customers rather than the roadmap. The Slack and iMessage bots shipped with no expectation that anyone would care — and became a favourite among founders and growth leads, whose work mostly happens outside the inbox. Instead of keeping Gmail open and refreshing it, they get texted when something important lands, which removes the context switching more than it saves clicks.
The second: people started using it as an executive assistant for scheduling, which it was never designed to be. The team knew it could draft, prep calls, label, and track follow-ups; customers worked out the rest.
The failure is more interesting. They shipped tab-complete — autocomplete for the next sentence — and nobody used it. People keep asking for the feature to this day, having ignored it when it existed.
What to automate first
Asked what a ten-person company should automate on day one, his answer is deliberately unglamorous: not replies, but reminders that you forgot to reply.
His view is that people badly underestimate how many important emails they drop in a week — a missed follow-up, a customer left waiting — often without receiving much mail at all. He sets a hard bar for non-technical roles: unless you’re in a meeting, there’s almost no excuse for taking longer than ten minutes to reply.
For his own mail, essentially everything starts as an AI draft and gets tweaked, usually for things software can’t do, like attaching a screenshot.
Cold email that actually lands
His advice runs against the usual instinct. Personalising what your product does for someone rarely moves the needle — if they have the pain, they were already interested; if they don’t, listing features won’t create it.
What moves the needle is evidence you looked into the person. Find a genuine interest, connect it to something you share, and lead into the product from there. It reads as written for them, it’s hard to get wrong, and the data is easy to find, whereas guessing what a company needs is easy to get wrong.
What’s next
The stated one-year goal is narrower than the ambition: brand recognition. Today, “email for high performers” makes people think of Superhuman, Shortwave, or Fyxer. He wants that reflex to be Slashy. His view is that once the brand sits at that level, most other problems solve themselves.
Find Harsha at slashy.com, and watch the full episode on YouTube. For another founder’s take on building a product people keep paying for, see our episode with James Zammit of Roark.
