Six years into his tenure as a partner at Sequoia Capital, Kais Khimji walked away. Not because of burnout, or a better offer, or the pull of another firm. He left to solve what he described as a decade-long obsession: the soul-crushing tedium of scheduling meetings.
The product that emerged from that fixation—Blockit, an AI agent that autonomously negotiates calendar slots—just closed a $5 million seed round. Leading the investment? Sequoia itself.
There's a certain symmetry to that arrangement, though perhaps not the sentimental kind. Pat Grady, the Sequoia partner who wrote the check, has known Khimji long enough to recognize when conviction runs deeper than professional courtesy. And the numbers backing Blockit suggest this wasn't simply a bet on an ex-colleague: the startup claims to have scheduled more than 100,000 meetings across 200-plus companies—Brex, Together.ai, Andreessen Horowitz, and Accel among them—without spending a dollar on customer acquisition.
That kind of organic traction gets attention in a market drowning in AI pitches.
How the Thing Actually Works
Strip away the buzzwords and Blockit functions as something close to a "self-driving calendar," though even that phrase undersells the mechanics. Users loop the AI into email threads via CC or ping it through Slack. From there, the agent takes over—juggling Google and Outlook calendars, navigating time zones, deciphering conflicting priorities, and negotiating available slots across multiple participants.
Unlike Calendly, which requires recipients to click through to a booking page and manually select from preset times, Blockit operates with more autonomy. It learns user preferences over time: whether you sign off emails with "Best regards" or "Cheers," whether mornings are sacrosanct or fair game. When two Blockit users try to schedule with each other, their respective agents negotiate directly, cross-referencing both calendars to identify optimal windows. No human back-and-forth required.
The company is emphatic about one point: "entirely AI, no humans in the loop." That's a deliberate jab at earlier attempts to automate scheduling—x.ai and Clara Labs, specifically—both of which relied on human assistants behind the curtain and eventually collapsed or sold for parts. Khimji and his co-founder, John Han, argue that recent leaps in large language models have finally made fully autonomous scheduling viable. Whether that's true, or just the latest iteration of overconfidence, will reveal itself in the product's ability to handle edge cases at scale.
The Network Effect Gamble
Sequoia's investment thesis, as Grady outlined in a launch post, hinges on network effects. The more users adopt Blockit, the more valuable it becomes—particularly when scheduling involves two users whose AI agents can both access full calendar context and negotiate in real time. Grady's framing positions Blockit not as software replacing software, but as a tool replacing labor. That distinction, in theory, unlocks "high monetization potential."
Blockit is pricing accordingly: $1,000 annually for individual licenses, $5,000 for team subscriptions, with a 30-day trial up front. Those figures land well above consumer-grade scheduling tools like Motion, which charges $19 to $29 per seat monthly, but target a different constituency—executives and founders for whom calendar chaos represents an existential bottleneck.
The company has also secured SOC 2 certification and pledged not to train AI models on customer data. Basic hygiene for enterprise adoption, but worth noting given how often startups stumble over those assurances.
Two Founders, One Shared Calendar Fixation
Han's background makes intuitive sense for a scheduling startup. He spent a decade-plus working on calendar products: first at Timeful, the AI scheduling app Google acquired in 2015, then years embedded within Google Calendar itself. Later stints at Clockwise and Retool kept him immersed in calendar infrastructure, the kind of niche expertise that breeds both insight and mild mania.
Khimji's trajectory is harder to trace. He studied politics, philosophy, and economics at Harvard before joining Sequoia in 2019—hardly the résumé of someone who'd spend six years thinking obsessively about calendar APIs. His LinkedIn post announcing Blockit describes being "obsessed with this idea for over a decade," a claim that feels either hyperbolic or revealing, depending on how charitable you're inclined to be. Maybe that's precisely the advantage: approaching the problem not as an engineer steeped in calendar minutiae, but as a user perpetually buried under scheduling overhead.
Other backers in the round include Haystack, Adjacent, Original, and Jeff Weiner—LinkedIn's former CEO—through his firm NPV. Weiner's involvement carries weight. Anyone who's run a company the size of LinkedIn has lived through the scheduling apocalypse that comes with coordinating executives, board members, and multi-continent teams. If he's willing to put money behind Blockit, the pain point is real.
The Graveyard of Smart Scheduling Tools
Calendly remains the category's $3 billion gorilla, though recent layoffs hint at mounting competitive pressure. A crop of AI-native challengers—Reclaim.ai, Motion, Amie—has emerged in the last few years, each emphasizing continuous optimization or task orchestration.
So what's different this time? Why should Blockit succeed where x.ai and Clara Labs failed?
Khimji points to what he calls an "agent harness"—the infrastructure required to make LLMs handle edge cases reliably. Group scheduling. Last-minute changes. Multi-party negotiation across conflicting constraints. The phrase is vague enough to mean almost anything, but the underlying point holds: earlier AI schedulers ran headlong into the long tail of weird, human scheduling scenarios and couldn't recover.
Whether Blockit's underlying technology is genuinely more robust or just benefits from better LLMs remains an open question. The 100,000 meetings figure suggests the product works for early adopters willing to hand control of their calendars to an algorithm. What's less clear is whether mainstream users—many of whom still bristle at automation creeping into personal workflows—will follow.
A Bet on Inevitability
Scheduling is one of those problems that feels trivial until you try to solve it. The logistics are deceptively simple: find a time that works for everyone. The execution is nightmarish. Time zones. Preference hierarchies. Calendar permissions. The subtle politics of who accommodates whom. No wonder it's littered with failed startups.
Blockit's advantage, if it has one, may be timing as much as technology. LLMs have improved dramatically in the last 18 months. Sequoia's backing provides both capital and credibility. And the product has already demonstrated traction in a skeptical market.
But the real test won't be adoption among tech-forward companies already comfortable handing workflows to AI. It'll be whether Blockit can convince the rest of us—people who still write "Does 3pm work?" in emails because it feels less robotic than automation—that the trade-off is worth it.
For now, Khimji has made his bet. He left one of the most prestigious venture firms in Silicon Valley to build software that negotiates calendar slots. Either that obsession was justified, or he's about to learn why so many smart people have tried and failed before him.
