Vimal KhannaCEO| mCalibre TechnologiesNew Delhi, India
Learnt that Anthropic has included contents of my Amazon #1 Best Seller book “Leading and Motivating Global Teams - Integrating Offshore Centers and the Head Office” in datasets used to train its AI models.
To me, this highlights how important the topic of dealing with challenges in leading distributed teams has become—and how relevant the practical solutions suggested in my book can be for addressing these challenges.
Pls share the top challenges you have faced in managing globally distributed teams and how have you addressed the challenges
Product Operations Program ManagerBarcelona, Cataluña, Spain
Hi Vimal, how can you know if your book has been used for Anthropic training (it is not open source)?
One of the main challenges is to strike the sweet spot between asynchronous and asynchronous work and ensure that all stakeholders are managed in a seamless manner.
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1 reply by Vimal Khanna
May 11, 2026 12:06 AM
Vimal Khanna
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The story is that Anthropic illegally picked up contents of some books from a site that hosts pirated books. It trained its AI models based on the books' contents. Case was filed and Anthropic has reached an out of court settlement to pay book publishers and authors. I received a mail to the effect and got aware of the case.
Saving Changes...
Vimal KhannaCEO| mCalibre TechnologiesNew Delhi, India
May 09, 2026 2:45 PM
Replying to Eduard Hernandez
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Hi Vimal, how can you know if your book has been used for Anthropic training (it is not open source)?
One of the main challenges is to strike the sweet spot between asynchronous and asynchronous work and ensure that all stakeholders are managed in a seamless manner.
The story is that Anthropic illegally picked up contents of some books from a site that hosts pirated books. It trained its AI models based on the books' contents. Case was filed and Anthropic has reached an out of court settlement to pay book publishers and authors. I received a mail to the effect and got aware of the case.
...
1 reply by Eduard Hernandez
May 12, 2026 4:19 AM
Eduard Hernandez
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Thanks for the heads up. This is an important issue in the ongoing development of LLMs. Since most models are not open source, it is often unclear which resources were used in their training. I’m glad to hear that, in this case, you were able to receive compensation for the unauthorized use of your materials.
Program Manager| HARPER SRLSanto Domingo / Distrito Nacional, Dominican Republic
One of the biggest challenges is keeping alignment without creating meeting overload.
With distributed teams, communication gaps appear very easily, especially around priorities, decisions, and expectations.
Making decisions and ownership very visible makes a big difference, so people are not dependent on being in the same timezone or in every conversation. Saving Changes...
One of the biggest challenges I’ve faced with globally distributed teams is that misalignment tends to compound silently.
At first, everything can appear healthy:
the meetings happen
Jira tickets are moving
status reports look green
…but teams may still have very different interpretations of:
priorities
urgency
ownership
or even what “done” means.
Timezone separation amplifies that because small misunderstandings often sit unresolved for an entire workday before someone notices.
What helped most in my experience wasn’t increasing meetings — it was improving operational clarity.
A few things that made a major difference:
Clear ownership for decisions and deliverables
Written decision logs so context wasn’t trapped in meetings
Consistent operating cadence across regions
Shared dashboards and metrics everyone trusted
Explicit prioritization when trade-offs were required
Rotating meeting times occasionally so the burden was shared fairly
I’ve also found that distributed teams work best when communication becomes intentionally asynchronous rather than trying to force everyone into constant real-time collaboration.
One of the hardest leadership lessons is realizing that alignment does not happen naturally at scale — especially across cultures, functions, and time zones. It has to be designed into the operating model. Saving Changes...
Luis BrancoCEO| Business Insight, Consultores de Gestão, LdªCarcavelos, Lisboa, Portugal
One of the biggest challenges I’ve observed in globally distributed teams is that problems rarely emerge first in execution.
They emerge in interpretation.
Different locations may receive the same strategy, the same priorities, and the same objectives, yet interpret urgency, ownership, risk, escalation, and collaboration very differently based on local context, culture, incentives, and communication dynamics.
Over time, this creates a hidden fragmentation that is difficult to detect because operational activity continues to move.
Teams communicate. Meetings happen. Reports are shared.
But gradually:
• Assumptions diverge • Dependencies become less visible • Decisions lose coherence across regions • And integration starts depending on informal relationships instead of explicit structures
Many organizations try to solve this with more governance, more meetings, or more reporting layers.
In my experience, what helps most is something deeper:
• Creating explicit decision ownership • Building shared contextual understanding, not only information sharing • Strengthening integration mechanisms across functions and geographies • Protecting psychological safety across cultures and hierarchies • And maintaining continuous learning and feedback loops across the system
Technology, collaboration platforms, and now AI can significantly improve coordination.
But sustainable performance in globally distributed teams still depends fundamentally on trust, clarity, integration, and the ability to preserve coherent decisions across distance, complexity, and cultural variation.
Very important topic and an increasingly critical capability for modern organizations. Saving Changes...
Product Operations Program ManagerBarcelona, Cataluña, Spain
May 11, 2026 12:06 AM
Replying to Vimal Khanna
...
The story is that Anthropic illegally picked up contents of some books from a site that hosts pirated books. It trained its AI models based on the books' contents. Case was filed and Anthropic has reached an out of court settlement to pay book publishers and authors. I received a mail to the effect and got aware of the case.
Thanks for the heads up. This is an important issue in the ongoing development of LLMs. Since most models are not open source, it is often unclear which resources were used in their training. I’m glad to hear that, in this case, you were able to receive compensation for the unauthorized use of your materials. Saving Changes...
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