A few weeks ago, I shared with my manager at the College of Engineering that I would be traveling to Malawi, and that I’d be attending our annual end of year event with our Applied Engineering students: ‘Design Day’ virtually.
I recall her asking, with utmost care:
‘Please pardon my ignorance, but will you have a stable internet connection?’
I giggled, as I respected her concern and ask, and assured her I would have internet connection. Malawi has internet connectivity… right?
I landed in Malawi on the 15th of April, and the shift in perspective was immediate.
On the drive from Lilongwe to Mangochi, something as basic as viewing tweets and TikTok videos became incredibly difficult. On arrival at home in Mangochi, the internet stabilized a bit. However, the upload of my documents [something I realize I do plenty of in my line of work] still stalled. Logging into email required multiple attempts. Connectivity would appear briefly, then drop, then return just long enough to create the illusion of continuity before disappearing again. It was not a single failure point, but rather a pattern—intermittent, uneven, and persistent enough to shape how one operates.
As my sister and I drove around Malawi from Lilongwe to our home district, Mangochi, I found myself paying closer attention to the physical environment. Power infrastructure felt inconsistent. Development was uneven. There were a lot of buildings still in construction, businesses that seemed to be in the process of forming rather than fully established systems. None of this was unfamiliar. I was however, perhaps for the first time in 5 years, seeing it through a very new lens.
After spending the past 5 years in environments where infrastructure is largely taken for granted, ‘returning’ to Malawi has made the underlying systems visible again. Perhaps more visible than it has ever been to me.
Lessons Conversation
Delve into Business and International Development with Nthanda Manduwi
10,000 Hours, and How Artificial Intelligence Can Get You There [Faster?]
This past week, I was delighted to join Dr. Vera Kamtukule – former Minister of Tourism in Malawi, as a guest on her new podcast: The Leadership Lab with Dr VK.
We got into conversation about entrepreneurship, about innovation, and about whether Africa is truthfully prepared and ready to partake in the fourth industrial revolution.
This is part 1 of a 3-part conversation. Have a listen, and please subscribe to her channel, so you do not miss the next episodes.
Two Questions
I recently finished reading two books:I Am Not a Robot, by Joanna Stern and Co-Intelligence by Prof. Ethan Mollick. I found the pairing useful because the two books approach AI from different but complementary directions.
Mollick’s Co-Intelligence is primarily concerned with how people can work with AI. His framing is I find extremely practical: how to collaborate with AI, how to remain the human in the loop, how to use AI as a co-worker, tutor, coach, or creative partner, and how to adapt to tools that are still improving rapidly.
Stern’s I Am Not a Robot approaches the question from the side of lived experience. Her work is less about AI as an abstract technical system and more about what happens when AI enters daily life: work, learning, intimacy, decision-making, productivity, attachment, automation, and the uneasy boundary between assistance and replacement.
What I found interesting is that very little in these books felt completely new to me – this was a delight. I do not say this in any way to criticise these books. It is likely just [great!] evidence that I have become an extreme AI user over the past two years.
Business school did that to me. The workload required reading, analysis, writing, presentations, strategy, modelling, research, and constant synthesis across different subjects. AI became part of how I managed that pace.
This is what I think both books do well: they give language to patterns many heavy AI users already experience but may not have fully named. Mollick helps explain how to work with AI deliberately. Stern helps explain what that work may be doing to us.
Ladder of Learning
In I am Not a Robot, Stern discusses the Bloom’s Taxonomy. First developed in 1956, the taxonomy organized learning objectives in the cognitive domain into levels: knowledge, comprehension, application, analysis, synthesis, and evaluation. It became one of the most widely used frameworks in education because it helped teachers and institutions think about different depths of learning.
In 2001, the taxonomy was revised by scholars including Lorin Anderson and David Krathwohl. The revised version shifted the categories from nouns to verbs and reordered the upper levels. The familiar revised sequence is: remember, understand, apply, analyze, evaluate, andcreate. This revision matters because it reframed learning as active performance rather than static possession of knowledge. A learner is not simply expected to have knowledge, but to do something with it.
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Four Rules
Mollick’s Co-Intelligence is useful because it frames AI not merely as a tool to be used occasionally, but as a collaborator that must be managed deliberately. The four rules he offers are quite practical in my opinion: always invite AI to the table; be the human in the loop; treat AI like a person, but specify what kind of person it should be; and assume this is the worst AI you will ever use.
This is the balance I keep returning to. AI is powerful enough to help people learn. It is also powerful enough to help people avoid learning. It can accelerate mastery. It can also simulate mastery.
Have a listenwherever you get your podcasts, or read the full article via my blog: Mastery.
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“Digital” Meant Access
My entry into technology was shaped by a much simpler framing of the digital divide.
At the time, the gap was understood primarily in terms of access. Those who had access to the internet could participate, create, and build. Those who did not were excluded. The solution, therefore, appeared straightforward: expand access, build skills, and enable participation.
I got into Tech as a blogger. Having the ability to create and publish online felt transformative, 15 years ago. It was both a tool and a gateway into a broader ecosystem. From that vantage point, it seemed clear that the work ahead was to extend that capability to others—to enable more people to build websites, create content, and engage with digital platforms.
That thinking informed a significant portion of my early work. One of the most advanced initiatives we built at the time was digitalskillsforafrica.com. It represented years of effort, with the intention of equipping more people to participate in the digital economy. Conceptually, it aligned well with the prevailing understanding of the problem.
In practice, adoption was far more complex.
Engagement did not scale as expected. The presence of the platform did not automatically translate into usage. At the time, I understood this as a challenge of execution. The adoption problem took me into and through my first Master’s program – where I analyzed the opportunities and challenges in Technology innovation and adoption in Malawi.
Looking at it now, it reads more clearly as a reflection of the system it was placed within.
Searching for AgTech Solutions
I left for the United States for the second time in 2024, on my way to the Michigan State University, with a specific question in mind:
what will it mean to provide AgTech solutions in a meaningful way?
At that point, my understanding was still grounded in the earlier definition of digital transformation. I was thinking in terms of tools, platforms, and improvements to existing processes. The assumption was that technology could be layered onto agriculture to enhance productivity and efficiency.
What I was not fully prepared for was how much the definition of technology itself had evolved.
In environments where infrastructure is stable and systems are continuous, technology is no longer treated as an external layer. It is embedded. Data flows continuously. Systems are designed to sense, respond, and adapt. Simulation is used before physical decisions are made. Automation is not experimental; it is becoming standard in certain contexts.
The question for me shifted from “how do we digitize processes?” to “how do we build systems that operate intelligently within the physical world?”
Physical AI
This is where the divergence becomes clear.
What was once considered “digital” has become foundational rather than advanced. Access to the internet, the ability to build websites, and participation in online platforms are no longer the frontier of technological development. They are the baseline upon which more complex systems are now being built.
The frontier has moved toward artificial intelligence, robotics, and cyber-physical systems—where intelligence is embedded directly into physical environments, and where systems are capable of operating with a degree of autonomy.
Returning to Malawi, I could see that both realities exist simultaneously.
On one hand, there is [some] visible progress. Young people are establishing offices, offering digital services such as photography, videography, graphic design, and website development. These are meaningful developments. They represent participation in the digital economy and should not be dismissed.
On the other hand, the global technological frontier is advancing in a different direction. The gap between these layers is not only one of speed; but also quite structural.
Entrepreneurial Opportunities
My earlier research on Malawi’s digital transformation provides a useful framework for understanding what I am now observing.
My research showed that the creation and adoption of digital systems are shaped by multiple conditions: technological readiness, organizational capacity, environmental context, and individual capability. Systems do not emerge in isolation. They depend on an ecosystem that can support their development and sustain their use.
It also became clear that adoption is driven by perceived benefit. People engage with systems that provide clear and immediate value within their context. What I have come to understand as the ‘pain point‘ in my business studies. External support plays a significant role in enabling both creation and adoption, particularly in environments where resources are constrained.
Seen through this lens, the behavior of the system in Malawi is not surprising. The environment is not failing to innovate. It is producing outcomes that are consistent with its conditions.
A Layered Divide
What has changed is not simply the scale of the digital divide, but its structure.
The earlier framing—those with access versus those without—no longer captures the full picture. The divide is increasingly defined by the layer of the technological stack within which different environments are operating.
This creates a layered divide.
Some environments are still consolidating foundational layers: connectivity, access, and basic digital services. Others have moved into building higher-order systems that integrate data, intelligence, and physical processes.
The implication is not just that some regions are behind, but that they are operating within a different segment of the technological trajectory. Movement between layers is not automatic. It requires the alignment of multiple conditions, many of which are outside the control of individual actors.
Perception and Constraint
Another critical factor is how value is perceived.
The adoption of earlier digital tools—websites, design services, media production—has been driven by their clear and immediate utility. These tools solve visible problems and fit within existing economic structures.
By contrast, more advanced systems—such as robotics or cyber-physical infrastructure—require a different kind of ecosystem to demonstrate their value. Their benefits are often systemic rather than immediate, and they depend on underlying infrastructure to function effectively.
As a result, innovation tends to concentrate within areas where its value can be both realized and recognized.
This does not indicate a lack of ambition, but a rational response to the environment.
Limitations on Imagination
There is also a more subtle effect at play.
The environment does not only shape what is built, but also what is imagined.
Reflecting on my own trajectory, I can see how earlier constraints influenced the scope of what felt possible. Certain ideas—particularly those involving complex, integrated systems—felt distant, more conceptual than practical. For me, it was not a question of capability, but of context.
Exposure to different environments expanded my boundaries. I was able to think of myself as someone who can build simulations, thanks to Xbox. I am newly still trying to see myself as someone who can build autonomous systems, thanks to Detroit. The past few years in the US have made it possible to see these systems as buildable rather than hypothetical.
Visiting Malawi now, the contrast is clear. The same environmental constraints that shape infrastructure also shape imagination.
We Need to Realign
If these dynamics persist, the outcome is not simply a difference in access, but a divergence in the types of systems that different regions are able to develop and sustain.
Some will build and operate advanced, integrated systems that combine intelligence and physical infrastructure. Others will remain focused on earlier layers of digital participation.
This is less a matter of individual effort, and more a function of systemic conditions.
I see it much much differently now. I used to think of the digital divide as a question of access.
What I am beginning to understand is that it is increasingly a question of depth.
It determines not only whether one participates in the digital economy, but at what level of the technological stack that participation occurs.
Returning to Malawi has made that distinction visible in a way that it had not been before.
Both the progress and the gap are real.
And understanding that tension is, for me, the starting point of the work ahead.
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