Does Stronger AI Make Companies Lighter?
When a company decides to fully embrace AI, should HR already have layoff lists ready on their desks?
This has been the most unspoken—and mistaken—assumption in the workplace over the past two years. We instinctively believe that the smarter the tools, the lighter the organization. Those intelligent work agents embedded into workflows seem purpose-built as “digital scissors” for cutting headcount.
But reality didn’t follow the plot of an uplifting novel.
As diverse third-party AI assistants flooded the Lark ecosystem, the anticipated mass layoffs never materialized. Instead, Lark—the enterprise’s digital foundation—saw its daily active users and interaction frequency surge dramatically. Within Lark’s instant messaging, documents, calendar, video conferencing, and multi-dimensional spreadsheets, we witnessed unprecedented levels of collaboration complexity.
AI has indeed changed the granularity of work. Third-party AI office tools can instantly generate draft documents, rapidly distill conclusions after video meetings, or assist in extracting key information from approval workflows. Individual productivity has reached an all-time high.
Yet a counterintuitive phenomenon emerged: companies did not become lighter as a result.
Why? Because efficiency gains often trigger demand expansion. If writing an industry report shrinks from three days to three hours, your manager won’t grant you a two-and-a-half-day break—they’ll ask you to produce ten reports, each from a different analytical angle.
Tools flatten execution barriers—but simultaneously stretch task boundaries.
Blindly believing “AI inevitably leads to layoffs” treats the enterprise as a static capacity container. But the real business world is dynamically expanding. Higher individual efficiency and smaller organizational scale are simply not synonymous. When AI runs the first 99 tedious steps, organizations actually need *more* people—not fewer—to fiercely tackle the final, make-or-break hundred meters.
Complexity Explosion After Ecosystem Integration
When various third-party AI assistants and intelligent work agents flooded the Lark ecosystem, a new workplace illusion was born: You think you’re integrating magic with one click—but what you’ve actually integrated is a tangled, deeply rooted web.
Consider a typical everyday scenario.
Once third-party AI office tools achieve full interoperability with Lark’s instant messaging, multi-dimensional spreadsheets, documents, and calendar, linear workflows instantly transform into networked data flows.
An employee invokes an AI assistant in a group chat to extract information, then deposits the results directly into a multi-dimensional spreadsheet; next, they refine the project draft inside a document; finally, they align stakeholders’ availability via the calendar. Data shuttles across multiple components, and cross-departmental collaboration nodes multiply accordingly.
It looks highly efficient, right? But the devil lies entirely in the details.
Who verifies factual accuracy in AI-generated drafts? Who cleans the massive influx of data entering multi-dimensional spreadsheets? And when intelligent tools support decision-making across multiple business lines, who defines—and enforces—those critically sensitive permission boundaries?
Tool integration shatters information silos—but erects towering walls around system maintenance.
In actual organizational operations, every intelligent node added carries steep hidden management costs. Enterprises must invest more human resources to serve as “translators” and “janitors” bridging AI and business.
They maintain system stability, fact-check AI hallucinations, and restructure fragmented AI outputs into coherent corporate knowledge repositories.
This reveals a slightly harsh truth.
We always assume AI performs subtraction—cutting redundancy to leave only minimalism. Yet within a vast collaborative ecosystem like Lark, AI is actually performing multiplication.
What AI delivers is never minimalism—it’s complexity at a higher dimension.
Machines expand the boundaries of capability; humans must shoulder the heavy burden of governance. In this sense, companies don’t grow lighter—in fact, they grow heavier, bearing far larger volumes of data flow.
AI Cannot Walk the Final Hundred Meters
Cloud-based large models may appear omniscient—but once dropped into the messy terrain of concrete business operations, they often appear clumsy.
This is AI’s most subtle and persistent pain point: it cannot walk the final hundred meters for enterprises.
After third-party AI assistants integrate into the Lark ecosystem, they truly *can* summarize video meeting highlights, instantly draft documents, or help match available time slots in calendars.
But they will never understand why a particular video meeting *must* include that vice president who never speaks—or why a specific approval workflow requires special authorization at a certain node—or why data in a particular multi-dimensional spreadsheet must *never* be exposed to a given department.
Machines understand probability—but only humans understand human dynamics and boundaries.
Delivering that final hundred meters cannot be accomplished by code alone. It directly fuels surging enterprise demand for “digital implementers.”
These professionals must possess deep domain expertise. They translate vague managerial requirements into concrete workflows inside Lark; they map sprawling data assets and define the red lines for AI security audits.
The more powerful the tool, the more demanding this “translation” and “boundary-setting” work becomes.
You might assume introducing an intelligent work agent would let mid- and back-office teams disband on the spot. Reality is the opposite: you need *more* seasoned professionals—people who know the business inside out—to teach that agent how to behave like a seasoned employee.
AI handles infinite generation—humans hold ultimate accountability.
On those final hundred meters, technology recedes backstage—and the weight of management returns squarely to human shoulders.
Expanded Task Boundaries and New Roles
Efficiency gains have never lightened organizations—they merely enlarge business appetite.
When third-party AI assistants compress the time required for individual tasks to the absolute minimum, enterprises won’t let idle staff depart. Instead, they launch projects at ten times the scale—within Lark.
This is business’s most fundamental greed—and its most authentic operating logic.
A back-office team that once maintained just a few basic documents and calendars now orchestrates massive cross-domain information flows. Thus, new roles like “Lark Ecosystem Operator” and “Multi-Dimensional Spreadsheet Architect” have emerged.
They don’t write low-level code—but they hold the enterprise’s digital lifeline in their hands.
Their daily work involves stitching scattered business silos into precision gears—using Lark’s instant messaging, approval workflows, and multi-dimensional spreadsheets. They rationalize approval logic, build the structural framework of multi-dimensional spreadsheets, and ensure every video meeting’s output becomes a traceable, archived document.
The smarter the tools, the deeper the reliance on “business translators.”
AI shortens the time needed to complete any single task—but lengthens the enterprise’s list of *what it wants to accomplish*. As execution friction vanishes, strategic imagination loses its limits.
Higher individual efficiency and smaller organizational scale have never been equivalent.
Organizations haven’t grown lighter. They’ve simply assumed heavier commercial missions—powered by a stronger collaborative foundation.
The Causal Illusion Between Efficiency and Scale
People easily fall into a linear causal illusion: if one person can do the work of three, then the other two should be optimized out.
But business operations have never been simple arithmetic.
When third-party AI assistants and intelligent work agents flooded the Lark ecosystem—interwoven with instant messaging, documents, calendar, and approvals—they did not construct a cold, ruthless layoff machine. Instead, they built a digital foundation designed to navigate uncertainty.
On this foundation, information flow no longer depends on humans racing frantically—but on the self-consistency of a collaborative network.
That doesn’t mean humans exit the stage. On the contrary, as foundational execution shifts to tools, organizations begin probing deeper waters. Strategies previously shelved due to excessive communication overhead—and innovations scrapped because cross-departmental collaboration proved too difficult—are now reviving inside Lark’s multi-dimensional spreadsheets and video meetings.
Efficiency gains haven’t eliminated positions—they’ve reset their coordinates.
We assumed AI office tools would lighten our companies, overlooking one crucial reality: lightweight tools often carry the heaviest ambitions. As Lark stitches together scattered business silos, enterprises see not a ledger of reduced labor costs—but a map for expanding commercial frontiers.
Technology ruthlessly compresses time spent on predefined tasks—yet generously expands the territory of human collaboration.
Machines exhaust known efficiencies; humans forever explore unknown possibilities.