The transition from successful venture capitalist to operational startup founder is a well-trodden, yet challenging, path in Silicon Valley. For Kais Khimji, a six-year veteran and former partner at the elite firm Sequoia Capital, that transition represents the realization of a long-standing ambition, dating back to his student days at Harvard. Khimji recently unveiled Blockit, an ambitious AI-driven scheduling platform poised to fundamentally redefine how professionals manage their most precious resource: time. This launch is highly credible, underscored by a $5 million seed funding round led by his former employer, Sequoia, signaling profound institutional confidence in the company’s vision and leadership.
Sequoia’s commitment is not merely a courtesy; it is a strategic endorsement that places Blockit in the lineage of other founder-VCs who successfully leveraged their venture experience to build industry-defining companies, such as David Vélez, who transitioned from Sequoia partner to found the massive Brazilian digital bank Nubank. Pat Grady, Sequoia’s general partner and co-steward, publicly championed the investment, articulating an aggressive financial forecast: “Blockit has a chance to become a $1Bn+ revenue business, and Kais will make sure it gets there.” This valuation target immediately establishes Blockit not as a minor productivity tool, but as a disruptive force capable of carving out a significant niche in the rapidly evolving landscape of enterprise automation.
The Decades-Old Scheduling Paradox
The market for automated scheduling has long been characterized by incremental improvements rather than transformative breakthroughs. While numerous ventures, including now-defunct pioneers like Clara Labs and x.ai (the domain of which now belongs, coincidentally, to Elon Musk’s AI company), attempted to automate the logistical burden of coordinating meetings, they often succumbed to the complexity and nuance inherent in human communication. Their failure highlighted a fundamental flaw: scheduling is not just an algorithmic problem of finding an open slot; it is a problem of negotiation, prioritization, and contextual understanding.
Khimji and co-founder John Hahn—who brings deep product experience from pivotal calendar tools such as Timeful, Google Calendar, and Clockwise—assert that the timing is now ripe for true disruption, primarily due to the maturity of Large Language Models (LLMs). These advanced AI capabilities allow Blockit’s agents to move beyond simple slot-finding into sophisticated, seamless negotiation, bypassing the limitations that constrained earlier iterations of scheduling automation.
Khimji encapsulates the core inefficiency that Blockit seeks to eliminate with stark clarity: "It always felt very odd. I have a time database—my calendar. You have a time database—your calendar, and our databases just can’t talk to each other." This disconnect forces a manual, asynchronous communication loop—the endless back-and-forth email chains—that drains hundreds of hours annually from professionals across every industry. Blockit’s revolutionary approach hinges on enabling these "time databases" to communicate directly, transforming scheduling into an autonomous, agent-to-agent transaction.
Agentic Architecture: Moving Beyond Link-Sharing
The current market leader in the scheduling domain, Calendly, which achieved a $3 billion valuation based on its efficient link-sharing model, fundamentally relies on the user initiating the transaction and presenting their available time slots. This methodology, while effective for one-sided booking, remains reactive and lacks the necessary depth for complex, multi-party enterprise coordination where power dynamics, urgency, and internal priorities must be weighed.
Blockit is betting against the link-sharing paradigm. Instead, it deploys intelligent AI agents designed to handle the entire scheduling lifecycle autonomously. When a meeting is required between two or more Blockit users, their respective AI agents engage in direct, machine-speed negotiation. This system is designed to mimic the proactive and diplomatic capabilities of a high-level executive assistant (EA).
The mechanism for invocation is designed for simplicity within existing workflows. A user can summon the Blockit agent by merely including it in an email thread or tagging it in a Slack message concerning the meeting. The bot immediately extracts intent, identifies participants, and takes over the logistical execution, negotiating not just time, but location, medium (e.g., video conference link), and aligning these variables with the detailed, often subjective, preferences of all attendees.
Mastering Context Graphs and Subjective Intent
The true technical innovation Blockit leverages lies in its ability to internalize and act upon subjective user preferences—a capability far beyond the scope of traditional deterministic algorithms. Khimji emphasizes that the system can be trained to replicate the decision-making framework of a human EA. Users input specific, nuanced rules: which meetings are "nonnegotiable" and which are "movable" based on factors like current workload, travel, or energy levels.
For instance, a user might instruct the agent that skipping lunch is acceptable on days with an especially heavy meeting load. "Sometimes my calendar is crazy, so I need to skip lunch, and the agent needs to know that it’s okay to skip lunch," Khimji explained.
More critically, Blockit can prioritize meetings based on contextual cues embedded within the communication itself, such as the formality or tone of the initial request. An instruction might be: a meeting request signed off with the formal "Best regards" should automatically take precedence over a casual interaction concluding with "Cheers." This level of semantic and emotional understanding is where LLMs prove their worth, allowing the AI to capture the implicit hierarchy of tasks.
This sophisticated data capture aligns perfectly with the emerging concept of "context graphs," a framework articulated by venture firm Foundation Capital partners Jaya Gupta and Ashu Garg. In their analysis, they describe a massive, multibillion-dollar opportunity for AI agents to move beyond collecting simple data points to capturing the "why" behind every business decision. Context graphs represent the hidden logic, the subjective trade-offs, and the accumulated knowledge that previously resided only in a human mind. Blockit’s success hinges on its ability to build and constantly update these complex context graphs for each user, turning subjective preferences into actionable, automated negotiation parameters.
Industry Implications and the Automated Assistant Economy
Blockit’s entry signals a major shift in the productivity software market. If successful, it will not just compete with Calendly; it will establish an entirely new category—the AI Social Network for Time. This term highlights the network effects inherent in the system: the utility of Blockit increases exponentially as more users adopt it, enabling more seamless agent-to-agent negotiations without human intervention. The ultimate goal is to eliminate the concept of "finding time" altogether, replacing it with proactive, intelligent time allocation.
The implications extend far beyond mere convenience. In high-velocity environments like venture capital, legal services, and high-growth technology companies, time arbitrage is mission-critical. The time saved from eliminating logistical coordination can be reallocated to strategic work. This focus on high-value time savings is reflected in Blockit’s pricing structure. Following a 30-day free trial, individual power users are charged $1,000 annually, with team licenses—supporting multiple users and presumably deeper integration capabilities—priced at $5,000 annually. This premium, enterprise-grade pricing clearly targets organizations where the cost of administrative time saved significantly outweighs the subscription fee.
Early validation for this model is strong. Blockit is already deployed across more than 200 companies, including prominent AI firms like Together.ai, the recently acquired fintech Brex, robotics innovators like Rogo, and top-tier venture capital firms such as a16z, Accel, and Index. The immediate adoption by VC firms is particularly noteworthy, as these organizations operate on intense, rapid-fire meeting schedules and are highly sensitive to administrative friction.
The Future of Executive Assistance and Agent Autonomy
The rise of truly autonomous scheduling agents like Blockit compels us to reconsider the future role of human executive assistants. Rather than replacing EAs entirely, these tools are poised to automate the most tedious, repetitive, and time-consuming aspects of their work—specifically, the endless back-and-forth required for logistics. This automation frees up human EAs to focus on higher-level strategic support, such as anticipating needs, managing complex relationships, and synthesizing information—tasks that still require human emotional intelligence and qualitative judgment.
Furthermore, Blockit’s approach is a microcosm of the broader trend toward agentic computing. As LLMs become more adept at not just generating text but executing tasks based on complex instructions, we are moving toward a digital ecosystem where various software agents negotiate resources, priorities, and schedules on behalf of their human principals. This transition represents a shift from static software tools to dynamic, communicative entities that understand the user’s intent within a specific context.
The challenge for Blockit, and indeed for the entire category of AI agents, will be maintaining trust and accuracy as the complexity of the scheduling environment increases. A human executive assistant can instinctively sense when a meeting request is crucial or when a minor scheduling conflict can be ignored. Blockit must prove that its context graph mapping and negotiation agents can handle the edge cases—time zone complexities, last-minute emergencies, and the shifting social dynamics of a meeting request—with the same diplomatic finesse and reliability as a seasoned professional.
Khimji and Hahn are effectively building the first truly integrated communication protocol for professional time. By treating calendars not as isolated data silos but as interconnected databases requiring intelligent, autonomous negotiation, Blockit aims to solve the scheduling paradox that has plagued knowledge workers for decades, positioning itself to capture the billion-dollar revenue opportunity envisioned by its initial investor. The success of this venture will determine whether LLM-powered agents can finally transition administrative automation from a helpful feature to a fully trusted, autonomous enterprise system.