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By:

Rahul Kulkarni

30 March 2025 at 3:32:54 pm

The Boundary Collapse

When kindness becomes micromanagement It started with a simple leave request.   “Hey, can I take Friday off? Need a personal day,” Meera messaged Rohit. Rohit replied instantly:   “Of course. All good. Just stay reachable if anything urgent comes up.”   He meant it as reassurance. But the team didn’t hear reassurance. They heard a rule.   By noon, two things had shifted inside The Workshop:   Meera felt guilty for even asking. Everyone else quietly updated their mental handbook: Leave is...

The Boundary Collapse

When kindness becomes micromanagement It started with a simple leave request.   “Hey, can I take Friday off? Need a personal day,” Meera messaged Rohit. Rohit replied instantly:   “Of course. All good. Just stay reachable if anything urgent comes up.”   He meant it as reassurance. But the team didn’t hear reassurance. They heard a rule.   By noon, two things had shifted inside The Workshop:   Meera felt guilty for even asking. Everyone else quietly updated their mental handbook: Leave is allowed… but not really. This is boundary collapse… when a leader’s good intentions unintentionally blur the limits that protect autonomy and rest. When care quietly turns into control Founders rarely intend to micromanage.   What looks like control from the outside often starts as care from the inside. “Let me help before something breaks.” “Let me stay involved so we don’t lose time.” “Loop me in… I don’t want you stressed.” Supportive tone.   Good intentions.   But one invisible truth defines workplace psychology: When power says “optional,” it never feels optional.
So when a client requested a revision, Rohit gently pinged:   “If you’re free, could you take a look?” Of course she logged in.   Of course she handled it.   And by Monday, the cultural shift was complete: Leave = location change, not a boundary.   A founder’s instinct had quietly become a system. Pattern 1: The Generous Micromanager Modern micromanagement rarely looks aggressive. It looks thoughtful :   “Let me refine this so you’re not stuck.” “I’ll review it quickly.”   “Share drafts so we stay aligned.”   Leaders believe they’re being helpful. Teams hear:   “You don’t fully trust me.” “I should check with you before finishing anything.”   “My decisions aren’t final.” Gentle micromanagement shrinks ownership faster than harsh micromanagement ever did because people can’t challenge kindness. Pattern 2: Cultural conditioning around availability In many Indian workplaces, “time off” has an unspoken footnote: Be reachable. Just in case. No one says it directly.   No one pushes back openly.   The expectation survives through habit: Leave… but monitor messages. Rest… but don’t disconnect. Recover… but stay alert. Contrast this with a global team we worked with: A designer wrote,   “I’ll be off Friday, but available if needed.” Her manager replied:   “If you’re working on your off-day, we mismanaged the workload… not the boundary.”   One conversation.   Two cultural philosophies.   Two completely different emotional outcomes.   Pattern 3: The override reflex Every founder has a version of this reflex.   Whenever Rohit sensed risk, real or imagined, he stepped in: Rewriting copy.   Adjusting a design.   Rescoping a task.   Reframing an email. Always fast.   Always polite.   Always “just helping.” But each override delivered one message:   “Your autonomy is conditional.” You own decisions…   until the founder feels uneasy.   You take initiative…   until instinct replaces delegation.   No confrontation.   No drama.   Just quiet erosion of confidence.   The family-business amplification Boundary collapse becomes extreme in family-managed companies.   We worked with one firm where four family members… founder, spouse, father, cousin… all had informal authority. Everyone cared.   Everyone meant well.   But for employees, decision-making became a maze: Strategy approved by the founder.   Aesthetics by the spouse.   Finance by the father. Tone by the cousin.   They didn’t need leadership.   They needed clarity.   Good intentions without boundaries create internal anarchy. The global contrast A European product team offered a striking counterexample.   There, the founder rarely intervened mid-stream… not because of distance, but because of design:   “If you own the decision, you own the consequences.” Decision rights were clear.   Escalation paths were explicit.   Authority didn’t shift with mood or urgency. No late-night edits.   No surprise rewrites.   No “quick checks.”   No emotional overrides. As one designer put it:   “If my boss wants to intervene, he has to call a decision review. That friction protects my autonomy.” The result:   Faster execution, higher ownership and zero emotional whiplash. Boundaries weren’t personal.   They were structural .   That difference changes everything. Why boundary collapse is so costly Its damage is not dramatic.   It’s cumulative.   People stop resting → you get presence, not energy.   People stop taking initiative → decisions freeze.   People stop trusting empowerment → autonomy becomes theatre.   People start anticipating the boss → performance becomes emotional labour.   People burn out silently → not from work, but from vigilance.   Boundary collapse doesn’t create chaos.   It creates hyper-alertness, the heaviest tax on any team. The real paradox Leaders think they’re being supportive. Teams experience supervision.   Leaders assume boundaries are obvious. Teams see boundaries as fluid. Leaders think autonomy is granted. Teams act as though autonomy can be revoked at any moment. This is the Boundary Collapse → a misunderstanding born not from intent, but from the invisible weight of power. Micromanagement today rarely looks like anger.   More often,   it looks like kindness without limits. (Rahul Kulkarni is Co-founder at PPS Consulting. He patterns the human mechanics of scaling where workplace behavior quietly shapes business outcomes. Views personal.)

How DeepSeek Is Making Silicon Valley Nervous

Updated: Feb 18

DeepSeek

Until recently, the artificial intelligence (AI) ‘arms race’ seemed like an all-American affair. OpenAI’s ChatGPT led the charge, with Google’s Gemini and Meta’s LLaMA models not far behind. American dominance in AI was assumed to be an inevitability, and an extension of Silicon Valley’s long-standing supremacy. Now, DeepSeek, the homegrown Chinese model has sent tremors through the AI industry, despite its baggage of state-backed propaganda.


DeepSeek’s emergence should have been unremarkable: yet another large language model (LLM), another iteration in a rapidly evolving space. But it has out-optimized its American competitors by achieving comparable - if not better - results through an optimized co-design of algorithms, frameworks and hardware. Since it is not just a matter of fewer parameters but also the algorithms operating on them more efficiently.


This has thrown a wrench into Meta’s grand AI ambitions. Meta, along with OpenAI and Google, have built their models on the assumption that more parameters mean better performance. Training these behemoth models requires staggering computational resources, and American tech firms have been quick to justify their exorbitant costs. But DeepSeek has shown otherwise. It has outperformed GPT-4o and Claude 3.5 Sonnet - the two US flagship models - on a series of standard and open-ended benchmarks.


Unlike its closed-source competitors, DeepSeek has open-sourced its model, allowing smaller players to adapt it without relying on subscription services of OpenAI or Anthropic. Small but clever modifications like the use of rotary embeddings and group relative policy optimization (GRPO) - a reinforcement learning paradigm - have led it to achieve impressive results without the usual computational bloat.


This has made American AI firms uneasy. Perplexity, a relatively small startup in the U.S., had to rely on post-training methods rather than foundational model training because it simply lacked the resources. Aravind Srinivas, CEO of Perplexity, has publicly noted how DeepSeek’s cost-effectiveness exposed flaws in the current American approach. Whereas OpenAI and Google charge sky-high fees for API access, DeepSeek offers a pricing structure - around $0.34 per 1,000 tokens - that undercuts them significantly.


For years, the prevailing assumption in AI research was that Western firms, with their access to the best talent and most powerful hardware, would remain untouchable. Yet, DeepSeek has shown that even modest improvements in model efficiency can disrupt the market. And now, other countries are taking notice.


American tech firms and policymakers alike have been quick to point out its ties to the Chinese government, warning of potential security risks and propaganda concerns. These concerns are not unfounded. AI models trained in authoritarian regimes inevitably reflect the biases of their environment, and DeepSeek is no exception. But to dismiss its technical achievements outright would be myopic. The reality is that DeepSeek’s advancements are not confined to China. Its innovations in model optimization can be repurposed by anyone. The backlash also smacks of a certain American hubris. Silicon Valley has long viewed itself as the sole architect of the AI revolution. When OpenAI and Google release new models, the conversation revolves around their transformative potential. When China does the same, the narrative shifts to fears of espionage and state control. It is in the interests of American firms to bash DeepSeek not just for geopolitical reasons, but because it threatens their bottom line.


While DeepSeek is unlikely to dethrone OpenAI or Google anytime soon, and its government ties will always make it a controversial player in global AI development, its existence has nonetheless forced a reckoning in Silicon Valley. It has shown that more efficient AI is possible and that cost need not be a barrier to entry. For the first time in a long while, Silicon Valley is feeling just a little bit jealous.(The author is a U.S.-based data scientist)

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