On arrival in Malawi, I did what any sensible manager would do: I made time to go spend a few hours with my most junior staff: our interns from the Malawi University of Science and Technology.
As per my intention, this was not meant to be a diagnostic exercise. I went in expecting to review their work, understand what had been built, and assess whether their output could transition to a new cohort I am onboarding in the United States. What I walked into instead was a much clearer view of a gap I had only understood in theory.
I am currently working across two very different environments. On one side, first-year computer science students at Michigan State University. On the other, graduating interns from the Malawi University of Science and Technology. The contrast between the two is not subtle. It is structural.
The difference is not intelligence. It is not effort. It is not even ambition.
It is something deeper in how capability is being formed.
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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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Gaps in Practice
The first signal was small.
I asked why a virtual meeting setup had not been implemented. They explained that they could not find the remote and decided to leave it. That answer carries more weight than it appears to on the surface. It reveals something about how problems are approached, whether they are pursued to resolution, and how ownership is distributed.
We moved on to the work itself.
I asked what had been built, and whether the work could be handed over to a new cohort. The responses were hesitant. There was no clear articulation of outputs, no structured understanding of what had been completed, and no confidence that the work could stand independently.
So I shifted the conversation.
I asked for their North Star—not for the internship, but for their lives. Who they wanted to become. What they were working toward.
The most common answer was financial independence.
It is an honest answer. It reflects the reality they are navigating. But it does not translate into a pathway. There was no clear connection between where they are today, the skills they are building, and the outcomes they are aiming for.
A Missing Layer: Industry Awareness
I then asked a simpler question.
What industry do you work in?
Eventually, the answer came: IT. I asked them to get specific about what science, and eventually Siphat was able to respond:
computer science.
I pushed further.
What is happening in computer science today? What is the state of your industry? What is being built? Where is it going?
No response.
In any technical field, industry awareness is foundational. It determines how individuals position themselves, what skills they prioritize, and how they understand their own relevance. Without it, skill development becomes detached from reality.
In contrast, I am working with first-year students in the United States who are already engaging with machine learning, artificial intelligence, and emerging systems. They track tools, companies, and developments in real time. They are not yet experts, but they are aware of the direction of their field, and that awareness shapes how they learn.
Skills Question
At this point, the conversation shifted toward skills.
Over the past 2 and a half month, the interns have been exposed to a range of tools and systems through their work with us—game design environments, simulation platforms, early exposure to robotics and IoT concepts, elements of AI and systems modeling. These are not arbitrary inclusions on my part, as I designed this programme. They are aligned with where I see the world moving, particularly within cyber-physical systems.
Yet exposure is not the same as capability.
When I asked what they could do, concretely, the answers were limited. Some referenced website design. Others mentioned general familiarity with tools. There was little evidence of depth in any one area, and little clarity about how those skills connected to a broader trajectory.
This is where the term “skill issue” is often used.
But the phrase is too blunt. It suggests that individuals have failed to acquire something they could have acquired. It assumes that the surrounding system is functioning as expected.
What I saw does not support that interpretation.
Expanding the Frame
To understand the gap, it helps to situate it within a broader technological context.
Globally, the frontier is shifting toward what is increasingly described as physical AI—systems where intelligence is embedded directly into the physical world through robotics, simulation, and autonomous infrastructure. This shift is already reshaping industries ranging from manufacturing to agriculture.
At the same time, countries like Malawi are still working to stabilize foundational layers of the digital economy. Expanding internet access, improving affordability, and building basic digital literacy remain central priorities. Large-scale efforts have made measurable progress. The World Bank’s Digital Foundations Project reports that more than 8.5 million Malawians gained access to high-speed, affordable internet between 2017 and 2024.
That progress matters.
It also makes clear where the system currently sits: consolidating foundational access at a time when the global frontier is already moving into more advanced, integrated systems. When one part of the world is building basic connectivity and another is advancing toward autonomous infrastructure, the gap that emerges is not incremental. It compounds.
The Data
This is not an isolated observation.
Across Malawi’s labor market, employers are already flagging the consequences. Skills gaps are increasing onboarding and retraining costs while reducing productivity and competitiveness. At the same time, a significant proportion of young people remain outside employment, education, or training, reflecting a system where education is not translating cleanly into economic participation.
Youth themselves identify a mismatch between what they are taught and what the market requires. Training exists, but it does not consistently convert into deployable capability or clear economic pathways.
These patterns align directly with what I saw in that room.
Students progressing through technical programs without a clear understanding of the industries they are entering – or perhaps I should say industries we have yet to create. Skills that remain shallow or misaligned. Ambition that is present, but not structurally supported.
“Skill Issue” Is Not Enough
It would be easy to stop here.
To say that the problem is a lack of skills. To recommend more training, more workshops, more exposure.
That explanation is incomplete.
Skills do not develop in isolation. They are produced within systems that define what is taught, how it is taught, and why it is taught. They are shaped by exposure to industries, access to tools, and expectations about what is possible.
When those systems are misaligned, the output reflects that misalignment.
What I saw was not a failure of individuals to acquire skills. It was a reflection of a broader structure that is not fully connecting education, industry, and future direction.
Layers Beneath the Gap
What is often labeled a skill gap is better understood as a combination of several interacting layers.
There is an exposure layer, which determines what individuals are able to see and engage with. There is an environmental layer, shaped by infrastructure and access. There is an expectation layer, which influences what people believe is possible or required. And there is an alignment layer, which determines whether education maps onto real economic activity.
In this case, all four are at play.
Students are operating in environments where connectivity is inconsistent, where exposure to advanced systems is limited, and where industries are not deeply integrated into academic pathways. Expectations are shaped by scarcity, and alignment between education and economic opportunity remains weak.
Under those conditions, the development of high-level, globally competitive skills becomes significantly more difficult.
Where We Go From Here
By the end of the session, I gave them a simple assignment.
Define your North Star. Identify who you want to be in the next five to ten years. Understand the industry you are entering. Map the skills required to get there. Present it clearly. We have the presentation schedule for before I fly out next week.
I think that this introduces structure where there was none.
But it does not resolve the underlying issue.
Because the question that stayed with me after that day was not about the students.
It was about the system that produced them.
Why does it exist in its current form? What was it originally designed to do? And where did the alignment between vision, education, and economic reality begin to break?
Those are the questions that matter.
And they require a different kind of answer.
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