My earliest lessons about distance were not about remote work; they were about life. I grew up in Maiduguri, far from most of the places where decisions about technology were made, and later moved to a foreign country where I did not speak the language and knew no one. The people I loved most were often thousands of kilometres away, yet I had to find ways to stay connected, to build trust, and to keep moving forward.
Years later, when I found myself leading AI teams spread across five continents, I realised that those early experiences had quietly prepared me for this moment. I already knew that physical distance does not have to mean emotional distance. I already knew that when you give people trust and autonomy, even if you are not in the same room or the same time zone, they will often do more than you imagined possible.
Times without numbers, distributed teams are treated as a degraded version of "real" teams — the ones who share an office, a whiteboard and a coffee machine. In reality, a well‑led distributed AI organisation can be more resilient, more diverse in thought, and more aligned with how modern AI systems themselves operate: decentralised, asynchronous, and constantly learning.
The challenge is not sending work to remote AI teams; it is rebuilding leadership, communication and culture so that distance becomes an advantage instead of a tax.
1. Why Distributed AI Teams Are a Different Animal
Not all remote work is created equal. A distributed marketing team, a remote customer support group and a global AI organisation face very different constraints.
- Heavy experimentation. AI work is experiment‑driven. A single week may involve dozens of model runs, data changes and metric analyses. Without a shared system, knowledge quietly fragments across laptops and time zones.
- Regulatory and data constraints. Data cannot always be freely moved across borders. A team in one region might have access to datasets that others cannot legally touch.
- Cross‑disciplinary collaboration. AI systems sit at the intersection of research, engineering, product, legal and safety. In a distributed setup, we must intentionally design how these voices meet.
- High cognitive load. Working with probabilistic systems that can fail in subtle ways is already hard; doing it across eight time zones without clear communication amplifies that difficulty.
If we simply copy the meeting patterns of a co‑located team into a distributed environment, we will exhaust people in one time zone while excluding those in another. At best, we slow down. At worst, we lose the very people we hired for their unique perspective.
2. Async‑First as a Leadership Philosophy, Not a Tool Choice
When people hear "async‑first," they often think of tools: issue trackers, chat, wikis. For me, async‑first is primarily a leadership philosophy. It begins with a simple assumption:
Nobody's best thinking happens in a rushed 30‑minute call at the end of their day; it happens when they are given context, space and trust to think deeply in their own time.
To honour that, we design communication such that:
- Information is written down once, clearly, instead of repeated in ten calls.
- People in different time zones can fully participate without sacrificing sleep or family.
- Decisions can be revisited later because the reasoning is documented, not trapped in memory.
2.1. The Three Layers of Async Documentation
Over time, I have come to rely on three main layers of documentation in distributed AI teams:
- Working notes. Free‑form scratchpads where ideas, hypotheses and half‑baked thoughts live.
- Experiment logs. Structured records of each run, with inputs, metrics, graphs and verdicts.
- Decision records. Concise documents explaining what we decided and why.
Working notes are personal; experiment logs and decision records are shared. Together they form the "memory" of the team.
2.2. Async Does Not Mean Silent
Async‑first does not mean avoidance of live discussions. It means we reserve synchronous time for what truly benefits from it: conflict, ambiguity, and creative connection. Everything else — status updates, experiment summaries, design drafts — happens in writing first.
| Topic | Async‑first by default | Sync‑first by exception |
|---|---|---|
| Status updates | Written weekly update, dashboard links. | Only when there is a critical incident. |
| Experiment results | Structured log with charts, comments thread. | Review call for high‑impact or surprising findings. |
| Architecture design | Design doc circulated for comments. | Live workshop after written feedback. |
| Conflict / misalignment | Short written context. | Immediate call; humans need voices and faces. |
3. Clear Experiment Tracking: The Remote Lab Notebook
In a co‑located team, much knowledge lives on whiteboards and in hallway conversations. In a distributed AI team, if it is not written down in a shared place, it does not exist.
I think of experiment tracking as a digital lab notebook for the entire team. Each experiment, no matter how small, is a first‑class citizen with:
- A clear hypothesis written in plain language.
- Explicit links to data versions, code commits and configuration.
- Before/after metrics with confidence intervals.
- A verdict: keep, discard or revisit.
The beauty of such a system is that it scales across locations. An engineer in Lagos can wake up, open the log, and immediately see what their colleague in Berlin tried overnight, complete with graphs and commentary. Instead of asking, "What did you do yesterday?" we can ask, "What did we learn yesterday?"
4. Outcome‑Focused Culture: Presence Is Not Performance
Perhaps the most dangerous habit to import into a distributed AI organisation is equating activity with impact. When we cannot see people physically, there is a temptation to measure them by how often they are "online", how quickly they respond to messages or how many meetings they attend.
This is a trap. In AI work, impact is not a function of keystrokes; it is measured in shifts in user outcomes, reliability, safety and long‑term capability.
| Input‑focused culture | Outcome‑focused culture |
|---|---|
| "Are you online during my hours?" | "Is the system better for users and safer than last month?" |
| Counts commits, messages, meeting attendance. | Tracks metrics, incidents, and learning velocity. |
| Rewards responsiveness. | Rewards good judgment and disciplined experimentation. |
| Optimises for visibility. | Optimises for impact. |
5. Building Trust Across Distance: Empowerment Without Micromanagement
My leadership philosophy has always been rooted in empowerment, trust and daring to fail. Distributed work does not change that; it amplifies it.
In an office, you can compensate for low trust with physical proximity. You can walk over to someone's desk, check on progress, read their body language. In a distributed team, you do not have that illusion. You either trust your people or you do not — and they can feel the difference in every interaction.
5.1. Clear Mandates, Wide Autonomy
For each person and each team, I try to articulate a clear mandate: the problem they own, the constraints they must honour, the metrics they are accountable for. Inside that mandate, I stay out of their way. I am present when they need support, but I resist the urge to peek into every notebook cell or every line of code.
5.2. Psychological Safety in Public Channels
In distributed setups, much communication happens in written channels that are visible to many people. Leaders must model how to respond to mistakes, half‑formed questions and failed experiments. When I thank someone publicly for surfacing an uncomfortable issue, I am not only rewarding honesty; I am teaching the whole team how we handle truth.
When something goes wrong — a batch job fails overnight in Singapore, a model drifts in São Paulo — I deliberately start with my own responsibility. Did we provide the right context? Did we make the trade‑offs explicit? Did we design the system so that a single oversight could cause this much damage? This posture does not absolve people of responsibility; it simply acknowledges that leadership owns the system in which choices are made.
6. The Operating System of a Distributed AI Team
Culture becomes real through rituals. These are some of the patterns that have consistently helped my distributed teams to stay aligned and effective.
6.1. Weekly Async "Heartbeat" Document
Every week, each team posts a concise heartbeat document, readable in five minutes:
- What we shipped.
- What we learned from experiments.
- Risks, incidents or ethical concerns.
- Help we need from others.
Comments happen asynchronously. If a topic needs deeper discussion, we schedule a call with the smallest necessary group, respecting time zones.
6.2. Distributed Design Reviews
For major architectural decisions, we follow a simple pattern:
- The proposer writes a design doc and shares it at least 48 hours in advance.
- People across time zones leave comments directly on the document, ideally before any meeting. This allows quieter voices and those with less overlap to contribute meaningfully.
- We hold a focused call, if needed, to resolve remaining disagreements. The final decision and rationale are recorded in an Architecture Decision Record.
6.3. Follow‑the‑Sun On‑Call
For systems that require 24/7 attention, we implement a follow‑the‑sun on‑call rotation. Engineers in different regions take responsibility during their daytime hours. Handover is done through a structured on‑call log, not through hurried voice calls.
7. Bringing People Together: The Power of Intentional Moments
An honest reflection: even the best async‑first system cannot fully replace humans seeing each other in person sometimes. For this reason, I treat gatherings — whether regional meetups or full‑team offsites — as investments in trust, not perks.
When we do manage to gather physically, we do not spend the time on status updates. We use it for:
- Retrospectives that go deeper than a comment thread ever could.
- Cross‑team brainstorming on long‑term bets.
- Unstructured time that lets people connect as humans, not just as avatars and usernames.
The paradox of distributed work is that a few intense, well‑designed in‑person moments can make the months of remote collaboration feel warmer, safer and faster. People are more willing to assume good intent from someone they have once shared a meal with.
8. In the End: Distance as a Source of Strength
Leading distributed AI teams is not about recreating the office in a video‑conferencing window. It is about accepting that the world has changed, that talent is everywhere, and that our systems — technical and human — must stretch to match that reality.
When we embrace async‑first documentation, clear experiment tracking and outcome‑focused cultures, something powerful happens. Engineers in wildly different places start to act with a shared sense of ownership. They make decisions aligned with the same values. They learn from each other across oceans and seasons.
Empowerment, trust and daring to fail are not constrained by geography. They travel as far as our willingness to write clearly, listen carefully and lead with courage.
The future of AI will be built by distributed teams who treat distance not as a disadvantage to be tolerated, but as a strength to be designed for.
If you are leading or joining such a team today, you stand at a unique intersection of possibility. You can build systems that serve millions, from places the world does not always see, with colleagues you may meet only a few times in person. Design your communication with intention. Anchor your culture in outcomes and ethics. Trust your people before they have "earned" it. The rest, I have found, has a way of following.
FAQ: Leading Distributed AI Teams
- How do you keep async teams aligned?
- Write once, share widely. Keep a single source of truth for goals, experiment logs, and decision records so every region can act independently.
- What rituals replace constant meetings?
- Weekly async heartbeat docs, written design reviews before any live call, and small, time-zone friendly syncs only for conflict or ambiguity.
- How do you prevent trust issues across time zones?
- Define clear mandates with metrics, make outcomes—not online presence—the currency, and respond to mistakes with learning-focused postmortems.