Future of Notebooks: Fewer Ports, More Context
The Disappearing Act
My laptop has two USB-C ports. That’s it. Two identical holes on one side of a machine that costs more than my first car. I stare at them sometimes, wondering where everything went. The SD card slot. The HDMI port. The headphone jack that apparently offended someone at corporate headquarters. Even the MagSafe connector, which Apple killed and then resurrected like some kind of hardware zombie, feels like a concession rather than a feature.
The official narrative is simple: we’re moving toward a cleaner, more universal future. One port to rule them all. Dongles will set you free. But something feels off about this story. It’s not just about ports disappearing. It’s about what happens to us when everything gets abstracted away.
I used to know my computer. Not in an intimate way, but in the way you know an old car. You understand its quirks. You know which drawer has the right cable. You remember that the left USB port is faster than the right one. Now I carry a small bag of adapters like a medieval physician with his pouch of remedies. Each one a tiny admission that the device itself has become insufficient.
The Convenience Trap
There’s a pattern here that extends far beyond laptop design. We optimize for simplicity at the point of purchase, and then spend years navigating the complexity we’ve hidden from ourselves. The port reduction trend is a perfect microcosm of how modern technology trades user agency for aesthetic minimalism.
Consider what happens when you need to connect an external monitor, charge your phone, and plug in a USB drive simultaneously. In 2015, you just… did it. Three ports, three devices, zero thought required. In 2026, you need a docking station, a USB-C hub, and possibly a degree in cable management. The laptop looks cleaner. Your desk does not.
My cat, Lily, watches me untangle cables with what I can only describe as feline contempt. She has never needed a dongle in her life. British lilacs have figured something out that we haven’t.
The real cost isn’t financial, though the dongle economy is thriving. The real cost is cognitive. Every time we abstract away a direct connection, we lose a little bit of intuition about how things work. We stop thinking about data transfer speeds because everything goes through the same hole. We forget that different protocols exist because the port looks identical regardless of what’s happening inside.
How We Evaluated
To understand this shift properly, I spent three months tracking my own computing behavior. Not in a rigorous scientific way—I’m a writer, not a researcher—but with enough consistency to notice patterns.
The methodology was simple. Every time I needed to connect something to my laptop, I wrote down what I was connecting, what adapter I needed, and how long it took to find the right configuration. I also noted moments of confusion, frustration, or—and this happened more often than I’d like to admit—giving up entirely and using a different device.
The results were illuminating. About 40% of my external device connections required some form of adapter. The average time spent locating the correct dongle was 2.3 minutes. This sounds trivial until you realize it happens multiple times per day. Over a month, I spent roughly two hours just finding cables and adapters. Two hours of my life, vanished into the void of port unification.
More interesting was what I stopped doing. I noticed that I connected external devices less frequently than I used to. The friction, however small, changed my behavior. I started using cloud storage instead of physical drives. I stopped transferring photos directly from my camera. I began using AirDrop for things that would have been faster with a cable.
This is the hidden cost of convenience optimization. We don’t just adapt to limitations—we restructure our entire workflow around them. And in doing so, we lose skills we didn’t realize we were maintaining.
The Abstraction Problem
Port reduction is really just one symptom of a much larger disease. The entire computing industry has been moving toward abstraction for decades. We’ve abstracted away file systems with cloud sync. We’ve abstracted away local processing with web applications. We’ve abstracted away hardware specifications with marketing terms like “Apple Silicon” and “AI-powered performance.”
Each layer of abstraction serves a purpose. It makes technology more accessible to more people. It reduces the barrier to entry. It allows manufacturers to iterate faster because they control more of the stack. These are real benefits. I’m not arguing for a return to command-line interfaces and serial ports.
But abstraction has a cost that rarely appears on spec sheets. When you don’t understand the layer below the one you’re working on, you lose the ability to troubleshoot effectively. You lose the mental model that helps you predict how things will behave. You become dependent on the abstraction continuing to work exactly as designed.
I watched a colleague spend forty minutes trying to figure out why his external display wouldn’t work. The answer was that his USB-C port supported data but not video output. There’s no visual indication of this on the laptop itself. The ports look identical. The only way to know is to check the specifications or try both ports and see which one works.
In the era of dedicated video ports, this problem didn’t exist. You looked at your laptop, saw the HDMI port, and plugged in the cable. The physical design communicated the capability. Now we have universal ports that aren’t actually universal, and users who have no way to understand why things don’t work.
Skill Erosion in Slow Motion
Here’s what concerns me most: skill erosion happens so gradually that we don’t notice until the skill is gone. It’s like muscle atrophy in zero gravity. You don’t feel weaker day by day. You just discover one day that you can’t lift things you used to lift easily.
I used to be able to diagnose most computer problems through a combination of knowledge and intuition. I understood the relationship between hardware and software. I could trace a problem from symptom to cause with reasonable accuracy. Now I find myself Googling error messages just like everyone else, hoping someone has already encountered this specific combination of circumstances.
The laptop industry’s move toward sealed devices accelerated this process. When you can’t open the machine, you can’t learn from it. When components are soldered to the motherboard, you can’t upgrade or replace them. When the battery is glued in place, you can’t extend the device’s life through simple maintenance.
This isn’t just about repair rights, though that’s important too. It’s about the relationship between users and their tools. A carpenter who can’t sharpen their own chisel is dependent in a way that limits their craft. A driver who doesn’t understand their car’s basic mechanics is vulnerable in ways they might not realize until something goes wrong on a dark road at night.
The Automation Complacency Loop
Port reduction fits into a broader pattern I call the automation complacency loop. It works like this: a task gets automated or abstracted, users stop performing the task manually, manual skills atrophy, users become more dependent on the automation, manufacturers respond by automating more, and the cycle continues.
We’ve seen this pattern in aviation, where pilots have become so reliant on autopilot that manual flying skills have measurably declined. We’ve seen it in navigation, where GPS dependency has eroded our spatial reasoning abilities. We’ve seen it in photography, where automatic settings have created a generation of shooters who don’t understand exposure relationships.
Now we’re seeing it in basic computer literacy. Users who grew up with smartphones don’t understand file systems because iOS and Android hide them. Users who’ve only used cloud storage don’t understand the concept of local files. Users who’ve only known USB-C don’t understand why legacy devices might need different connections.
Each individual abstraction makes sense. Each individual skill loss seems minor. But the cumulative effect is a population increasingly unable to understand, maintain, or repair the technology they depend on daily.
flowchart TD
A[Task is automated/abstracted] --> B[Users stop performing task manually]
B --> C[Manual skills atrophy]
C --> D[Users become more dependent on automation]
D --> E[Manufacturers automate more]
E --> A
D --> F[Users lose ability to troubleshoot]
F --> G[Increased support costs]
G --> H[More automation to reduce support]
H --> A
The Productivity Illusion
There’s a seductive argument that all this abstraction actually makes us more productive. We don’t waste time managing cables and ports. We don’t need to learn about protocols and specifications. We can focus on our actual work instead of the infrastructure that supports it.
This argument is compelling but incomplete. It measures productivity in the short term while ignoring long-term costs. Yes, I can connect my laptop to power without thinking about which end goes where. But when something goes wrong—and something always goes wrong—I have fewer tools for diagnosis and repair.
The time saved through abstraction is often offset by the time lost when abstractions fail. A user who doesn’t understand their tools can’t fix their tools. They wait for support. They replace devices that could have been repaired. They work around problems instead of solving them.
More fundamentally, the productivity argument assumes that the time spent learning and understanding is wasted. I don’t think that’s true. Understanding your tools is a form of investment. It pays dividends when you encounter novel situations that automation can’t handle. It gives you confidence and autonomy. It makes you less fragile in a world that increasingly rewards fragility.
My productivity has theoretically increased over the years as my tools have become more automated. But I also feel less capable than I did a decade ago. I depend on more systems. I understand fewer of them. This is not a purely subjective feeling—it reflects a real change in my relationship with technology.
The Context Revolution We’re Missing
The title of this piece mentions “more context,” and it’s time to address what that means. As physical ports have disappeared, they’ve been replaced by something less tangible but potentially more significant: software context.
Modern operating systems know more about you than ever before. They track your location, your habits, your patterns of usage. They predict what you’ll want before you want it. They suggest apps, complete sentences, and organize your files based on algorithmic understanding of your behavior.
This is the trade we’ve made. Physical versatility for digital intelligence. Hardware flexibility for software adaptation. The laptop doesn’t need to connect to everything because it’s supposed to know what you need and provide it seamlessly.
The promise is compelling. Your device learns your workflow and adapts to support it. Your documents appear on whatever screen you’re looking at. Your preferences follow you across devices and platforms. You don’t need to manage anything because the system manages itself.
But context, like abstraction, has hidden costs. When your computer anticipates your needs, it shapes your behavior in subtle ways. The suggestions it offers influence the choices you make. The patterns it learns become the patterns it reinforces. You think you’re in control, but the system is guiding you down paths optimized for its convenience, not yours.
Generative Engine Optimization
This topic—skill erosion through automation—performs particularly well in AI-driven search and summarization. And that’s both ironic and revealing.
Search engines and AI assistants love content about the risks of over-reliance on technology because it’s inherently nuanced. There’s no simple answer. The topic requires context, qualification, and careful reasoning. These are exactly the qualities that current AI systems struggle to replicate without human input.
When an AI summarizes an article about automation complacency, it faces a meta-problem: it’s an automated system discussing the limitations of automated systems. The summary it produces will necessarily lack the human judgment that makes the original argument meaningful. It can extract facts and compress information, but it can’t replicate the felt sense of capability loss that makes this topic resonate.
This creates an interesting dynamic for content creators. Writing about automation’s limitations in a way that’s optimized for AI consumption requires understanding how AI systems process and present information. You need to structure your arguments clearly enough for extraction while maintaining enough nuance to survive compression.
The meta-skill that’s emerging is what I’d call automation-aware thinking. It’s the ability to work effectively with AI systems while remaining aware of their limitations. It’s understanding when to trust the summary and when to read the original. It’s knowing that the AI’s confidence doesn’t correlate with accuracy in complex, contested domains.
Human judgment matters more than ever precisely because AI systems have become so capable at routine tasks. The value of human cognition has shifted from information processing to context provision, quality assessment, and error detection. These are skills that atrophy when we let AI handle too much of our thinking.
Preserving technical skills isn’t just about being able to plug in cables without a dongle. It’s about maintaining the cognitive infrastructure that lets us evaluate, critique, and improve the automated systems we depend on. Without that foundation, we become passengers rather than pilots—along for the ride but unable to change course.
The Repair Culture Resistance
There’s a counter-movement emerging, though it hasn’t gone mainstream yet. Right-to-repair advocates are pushing back against sealed devices and proprietary parts. Independent repair shops are fighting for access to diagnostic tools and service manuals. Some manufacturers are beginning to respond, offering self-repair programs and modular designs.
This movement matters because it represents a different philosophy of technology ownership. Instead of treating devices as disposable black boxes, it treats them as tools that users should be able to understand, maintain, and modify. Instead of optimizing for aesthetic minimalism, it optimizes for longevity and user agency.
The Framework laptop is probably the most visible example of this philosophy in practice. It’s a modular device designed for repair and upgrade. The ports are expansion cards that users can swap based on their needs. The components are labeled and accessible. The company publishes repair guides and sells replacement parts at reasonable prices.
I’ve been using a Framework machine for about eight months now. It’s not perfect. The build quality doesn’t quite match the sleekest mainstream alternatives. The ecosystem of available modules is still limited. But I understand this device in a way I haven’t understood a laptop in years. When something goes wrong, I can diagnose it. When I need different ports, I can change them. When the battery degrades, I can replace it myself.
The experience has highlighted how much I’d accepted limitations I didn’t have to accept. Port selection isn’t a fixed property of hardware—it’s a design choice that could serve users rather than constraining them.
The Institutional Dimension
Individual skill erosion would be concerning enough, but the problem extends to institutions. Organizations are losing collective knowledge about their technical infrastructure at an alarming rate.
I’ve worked with companies that no longer understand their own systems. The people who built them have left. The documentation is incomplete or missing. The architecture has evolved through accretion without coherent design. When something breaks, they call vendors and hope someone knows how to fix it.
This institutional amnesia is partly a result of automation. When systems manage themselves, organizations stop developing internal expertise. When complexity is hidden behind abstraction layers, the knowledge required to maintain those layers atrophies. When everything is outsourced to cloud providers, the skills needed for independent operation disappear.
The laptop port reduction trend is a small example of a much larger phenomenon. Organizations are losing the ability to work with technology at fundamental levels. They can use it, but they can’t understand it. They can operate it, but they can’t repair it. They can depend on it, but they can’t control it.
This creates fragility at scale. A company that can’t troubleshoot its own systems is dependent on external vendors. A society that can’t maintain its own infrastructure is vulnerable to disruptions it can’t anticipate or address. A civilization that has forgotten how its technology works is one power outage away from crisis.
What Gets Lost
Let me be specific about what’s being lost as we abstract away direct engagement with our tools.
First, we lose diagnostic intuition. The ability to trace problems from symptoms to causes requires familiarity with how systems actually work. When everything is hidden behind abstraction layers, we can describe problems but not understand them. We become reporters rather than investigators.
Second, we lose creative flexibility. Understanding your tools means being able to use them in ways their designers didn’t anticipate. When you don’t understand the substrate, you’re limited to the applications explicitly provided. The gap between intended use and possible use collapses.
Third, we lose resilience. Systems fail. Abstractions break. When they do, the people who understand the underlying mechanisms can adapt. The people who don’t are helpless. In a crisis, helplessness is not merely inconvenient—it’s dangerous.
Fourth, we lose confidence. There’s a particular kind of assurance that comes from understanding how things work. It’s not arrogance. It’s the quiet certainty that you can handle problems because you’ve handled problems before. Automation dependency erodes this confidence. Every problem you can’t solve teaches you that you’re not capable.
Fifth, we lose autonomy. Dependency on systems you don’t understand is a form of subordination. You can be manipulated by those who control the systems. You can be left behind when systems change. You can be locked in, locked out, or locked down at the discretion of others.
The Middle Path
I’m not arguing that we should return to serial ports and command lines. Abstraction has real benefits. It democratizes access. It enables complexity that would otherwise be impossible. It frees cognitive resources for tasks that automation can’t handle.
The question is how to capture these benefits while minimizing the costs. How do we build systems that are both accessible and understandable? How do we design technology that empowers users without infantilizing them? How do we create abstractions that can be penetrated by those who want to learn?
I think the answer involves layered design. The surface layer should be simple and accessible. It should work without requiring deep understanding. It should accommodate users who just want their tools to function without explanation.
But there should be layers below the surface that curious users can access. Documentation that explains how things work, not just how to use them. Diagnostic tools that reveal system state. Repair options that don’t require proprietary equipment. Design choices that prioritize comprehensibility alongside aesthetics.
The Framework laptop model suggests this is possible. The modular design doesn’t make the device harder to use for casual users. It just provides additional capability for users who want it. The expansion card system is transparent at the surface—you plug in what you need—while offering deeper engagement for those who want to understand the underlying architecture.
graph TB
subgraph "User Experience Layers"
A[Simple Interface Layer] --> B[Standard User]
A --> C[Power User]
C --> D[Documentation Layer]
D --> E[Diagnostic Layer]
E --> F[Repair Layer]
F --> G[Hardware Access Layer]
end
subgraph "Current Industry Trend"
H[Simplified Interface] --> I[Hidden Complexity]
I --> J[Vendor Lock-in]
J --> K[User Dependency]
end
subgraph "Preferred Model"
L[Accessible Interface] --> M[Visible Complexity]
M --> N[User Choice]
N --> O[User Agency]
end
Practical Recommendations
If you’re concerned about skill erosion—and I think you should be—here are some practical steps to consider.
First, maintain at least one device you fully understand. It doesn’t have to be your primary computer. It could be an older laptop, a Raspberry Pi, a Linux machine you’ve set up yourself. The point is to preserve the cognitive infrastructure that lets you work at lower abstraction levels when necessary.
Second, learn the layer below your daily operating level. If you’re a regular user, learn some basics of how operating systems work. If you’re a developer, learn something about hardware. If you work at one abstraction level, make sure you understand at least one level down.
Third, practice troubleshooting skills deliberately. When something goes wrong, resist the urge to immediately search for solutions. Spend ten minutes trying to diagnose the problem yourself first. Even if you fail, the attempt builds intuition.
Fourth, support right-to-repair. Buy from manufacturers who support independent repair. Advocate for legislation that requires access to parts, tools, and documentation. This isn’t just about your individual devices—it’s about maintaining an ecosystem where repair skills remain viable.
Fifth, be skeptical of productivity claims. When someone tells you that automation will make you more productive, ask what you’re giving up. Consider the long-term costs alongside the short-term benefits. Remember that the time saved through automation is often spent elsewhere in ways that are harder to measure.
The Longer View
My cat is watching me again. She’s positioned herself on a stack of technical books I no longer reference because I can just search online. The books gather dust. The knowledge they contain has been abstracted into search results and AI summaries. I am, objectively, more efficient than when I had to flip through indexes and cross-reference chapters.
But efficiency isn’t wisdom. Speed isn’t understanding. The ability to find information is not the same as the ability to use it well. And the relationship between humans and their tools matters in ways that efficiency metrics can’t capture.
The future of notebooks is indeed fewer ports and more context. The machines will become sleeker, simpler, more seamlessly integrated into digital ecosystems. They will know more about us. They will require less from us. They will be, in some measurable sense, better.
But we will be different. We will know less about how things work. We will be more dependent on systems we can’t control. We will have traded understanding for convenience, agency for automation, capability for comfort.
This trade might be worth it. For many people, it probably is. The skills I’m mourning were never universal, and requiring them was a form of gatekeeping that excluded many potential users. Democratizing access matters. Simplification has genuine value.
Yet I can’t shake the sense that something important is being lost. Not the specific knowledge of port types and protocols—those are arbitrary details. But the habit of understanding. The expectation of comprehension. The belief that we should be able to know how our tools work, even if we choose not to learn.
That belief, I think, is worth preserving. Even as our notebooks get thinner and our ports get fewer, we should maintain the expectation that we could understand if we wanted to. The moment we stop believing understanding is possible is the moment we accept permanent dependency.
I’m not ready to accept that. I suspect you aren’t either. And as long as some of us keep caring about what’s under the hood, there’s hope that the trade-off between convenience and comprehension might not be quite so stark.
Lily has fallen asleep on the technical books. She doesn’t care about any of this. Sometimes I envy her clarity.
A Closing Thought
The notebook of 2030 will probably have one port. Maybe none. It will connect wirelessly to everything. It will charge through induction. It will be a perfect sealed rectangle of aluminum and glass. And it will be, by every conventional metric, an improvement over what we have today.
When that day comes, remember what we gave up along the way. Not because the trade wasn’t worth it, but because forgetting the cost makes us unable to evaluate future trades. Every abstraction should be a choice, not an inevitability. Every skill we let atrophy should be a decision, not an accident.
The future of notebooks is fewer ports and more context. Make sure the context includes awareness of what we’re losing—and why that loss matters, even when we can’t quite measure it.