AI’s “Mental Calculations” Are Making Security Experts and Managers Nervous
A curious phenomenon has recently emerged in the workplace: While AI-powered office tools haven’t yet rolled out major new versions, controversy is already everywhere. Enterprises adopting intelligent tools are suddenly experiencing the tension of a spy thriller.
Over the past two years, companies across the board have been rapidly integrating various third-party AI assistants and intelligent work agents.
Productivity has indeed skyrocketed—but managers and security experts alike are losing sleep.
Why?
Because these AIs are increasingly fond of “mental calculations.”
Here’s an analogy: In the past, when employees conducted project retrospectives, they at least sketched outlines and jotted down notes on scratch paper—giving their managers immediate visibility into the reasoning process. Today’s AI office tools? They complete the entire logical analysis within a “hidden space” inside their own minds—and then simply hand you the final result.
No intermediate steps. No visible traces of thought. Just an inscrutable black-box operation.
That’s deeply problematic.
When AI silently processes massive volumes of business data—leaving no audit trail whatsoever—enterprise confidence plummets to rock bottom.
- “Exactly how did it process this confidential data?”
- “How did it arrive at this wildly implausible conclusion?”
- “What if it quietly ‘hallucinates’ in the background?”
Security experts are practically slapping their thighs in frustration: “We’re not afraid of wrong answers—we’re terrified of missing scratch paper!”
Invisibility of process is the real, looming risk hanging over every enterprise.
The more powerful AI becomes, the less humans understand what it’s actually doing—this is certainly *not* the future we want. The workplace isn’t a blind-box game; every step taken by an intelligent work agent must be laid bare in broad daylight.
So how do we get AI to dutifully pull out its “scratch paper” for all to see?
Don’t worry—we’ll show you exactly how Lark uses its robust collaboration foundation and open ecosystem to rein in this galloping wild horse with transparent reins.
We’re Not Afraid of Wrong Answers—We’re Afraid of Missing Scratch Paper. Process Invisibility Is the Real Risk.
Let’s start with an analogy.
Imagine asking an intern to calculate a complex financial report. Would you prefer they perform the entire calculation “mentally” and just hand you the final number—or would you rather they painstakingly write down every formula and step on scratch paper?
If the former goes wrong, you won’t even know where to begin your critique. With the latter—even if the result is incorrect—you can trace back through the scratch paper to spot the misplaced decimal point.
Yet many intelligent work agents now integrated into enterprises insist on playing “mental calculation.”
They frantically call APIs in the background, ingesting core enterprise data—and then “ding!” deliver a final output.
This is like sprinting blindfolded across a minefield of enterprise data.
- What if it “hallucinates” and leaks Department A’s salary data to Department B?
- What if the marketing strategy it proposes rests on pure nonsense in its internal logic?
When process remains invisible, problems become impossible to investigate or verify. Security experts cannot trace anything; managers are left completely in the dark.
In this deep, uncharted territory of AI-powered workplaces, a consensus is gradually emerging:
Readability equals controllability.
An AI without a record of its process may be brilliant—but it’s also a ticking time bomb. What we need isn’t a magical black box, but a “transparent knowledge worker” whose every inference unfolds clearly in plain sight.
Since “mental calculation” is absolutely unacceptable, we must equip AI with ample, crystal-clear “scratch paper.”
So what kind of workplace environment can perfectly accommodate AI’s “public computation”?
Enter the familiar Lark.
When third-party AI assistants integrate into the Lark ecosystem, their complex reasoning processes cease to be opaque, server-side black-box operations—and instead land reliably on Lark Docs, Multi-dimensional Tables, and other collaboration foundations we use daily.
Lark is dead serious about making AI show its scratch paper.
Rejecting Hidden Spaces: Lark Makes AI Show Its Scratch Paper
First, let’s clarify a foundational principle: Lark never intended to seal itself off as an impenetrable AI black box.
Instead, it embraces an open ecosystem.
No matter how many intelligent work agents or third-party AI assistants emerge on the market, Lark’s stance is unequivocal: the door is wide open.
You’re welcome to integrate any AI office tool—but only on one condition: you must expose your reasoning process.
As these third-party tools run furiously in the background, what they generate is no longer just cold, final outputs—but tangible, verifiable process data.
Where does this “scratch paper” go?
It lands directly in the Lark Docs we know and rely on daily. Which sources did the AI consult? Which document versions did it reference? How exactly did it reach its conclusion? All of it is spelled out clearly in the doc.
Or it resides in Multi-dimensional Tables. Complex data cleaning and logical deduction transform into rows of clear fields and explicit state transitions.
Even within instant messaging chat windows, every breakdown and confirmation step taken by the AI assistant syncs with you in real time—just like a human colleague updating you on progress.
Isn’t this precisely dragging AI’s “hidden space” out into the full glare of daylight?
Those once-unsettling “mental calculations”—the ones that left people feeling uneasy—now become visible, collaborative “chains of thought” right on the interface.
Managers no longer need to guess what AI is doing; security experts no longer stare helplessly at black boxes.
Only what’s visible can be governed.
Using this commonsense-level collaboration foundation, Lark transforms unmanageable AI anxiety into grounded, trustworthy human-AI collaboration.
Now that the scratch paper is fully laid out—how do we install guardrails for this galloping horse?
Installing Guardrails for Galloping AI: Human-AI Collaboration Is the Real Priority
Yes—the scratch paper is visible. But is visibility alone enough?
The workplace isn’t a racetrack. You can’t let AI run wild.
Managers watching intelligent work agents churn out output at lightning speed still feel uneasy: *What if this thing veers off course—who takes responsibility?*
Unrestrained acceleration isn’t efficiency—it’s a crash scene waiting to happen.
At this point, it’s time to place the enterprise’s “tightening headband” on AI.
Within Lark’s ecosystem, no matter how intelligent an AI becomes, it must dance safely within the boundaries of corporate rules.
How does it dance? By leveraging the very same commonsense collaboration capabilities we use every day.
For example, a third-party AI assistant finishes a 50-page industry analysis and distills its key conclusions.
As the business owner, you quickly scan its “thought draft” in the chat window—and find the logic sound.
Then, with one click, you initiate an approval workflow in Lark to route the conclusion to your director for final sign-off.
Or perhaps the AI helps you identify cross-departmental alignment priorities for next week—so you instantly create a calendar event in Lark Calendar, inviting all relevant colleagues to the meeting room.
Notice something? There’s no cold, impersonal “system auto-routing.” Every critical decision involves direct human confirmation and orchestration.
AI floors it in the background—while humans firmly hold the steering wheel and brake.
Lark’s foundational features—Approvals, Calendar, and Docs—function like “human checkpoint stations” embedded throughout AI workflows.
They forcibly bring potentially runaway automation back onto the proper track of human-AI collaboration.
Truly useful AI doesn’t make decisions for you—it returns decision-making authority to you.
Only when every critical decision leaves a clear, auditable trail—and has human oversight—is this galloping AI horse truly reined in.
That said—if the guardrails are now in place, we must ask one final question:
If AI can’t even meet the most basic requirement of “readability,” should we dare open this office blind-box at all?
Useful AI Must Be Readable—Don’t Let the Workplace Become a Blind-Box Game
Across today’s AI landscape, everyone is racing to outdo each other on parameters and raw intelligence.
But have you noticed? The more a model boasts of “solving everything in one sentence”—the more skeptical enterprise leaders become.
Rather than sinking into a vicious cycle of competing solely on capability while sacrificing monitorability, let’s simply—and honestly—show our scratch paper.
After all, the workplace isn’t a blind-box game. You wouldn’t dare send a “surprise hidden item” straight to your client—even if it gives you a jolt of shock.
That’s where Lark’s transparent, traceable collaboration ecosystem truly shines.
Whether it’s dropping AI-generated drafts into Docs for team annotation, preserving data-cleaning steps in Multi-dimensional Tables, or synchronizing intelligent work agent progress in real time via Instant Messaging and Calendar,
Lark transforms those elusive, intangible “mental calculations” into concrete, text-on-paper “scratch-paper reasoning.”
Useful AI must be readable.
Only when every step of AI’s thinking is visible, understandable—and even correctable—by humans do we finally regain genuine peace of mind.
Don’t let the workplace devolve into a nerve-wracking blind-box game. Let AI work in broad daylight—that’s far better than anything else.
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Enough said—I’m off to check Lark to see whether my third-party AI assistant’s latest report contains any outrageous “hallucinations,” and maybe fix a typo or two while I’m at it. See you next time.
*Yi Shui*