• Mastery used to be about skill and taste matching up, the slow repetitive practice to build up skills

• AI allows a higher volume of iterations, thus making ‘meta skills’ of editing and recognising value a lot more important, becoming more like a film director than a solo craftsperson.

• That tools enables speed, but success depends on intentional engagement, using iterations to shape thinking iteratively, rather than lazy copy-pasting.

What does it mean to be a master of something? To be a great programmer, designer, or writer? The path to mastery has always been the path of 10,000 hours. But what does that really mean? I believe that path is about iterating, making something, seeing how it's not quite right, and making tiny adjustments toward the next version. An almost unconscious process of getting better through repetition.

And what happens when AI tools enter that picture? I believe AI tools are changing, *expanding* what we mean by mastery and craftsmanship. They're challenging what we think of as 'getting good at something.'

When friction was the teacher

What does it mean to get good at a skill? To develop a craft? Getting good used to require helpful friction. The process where the process is ‘expensive’, and thus makes you invested in the outcome. Think of a photographer having to manually develop pictures. All that work for a single picture, makes you really understand all the nuances of skill required to get it, and makes you invested in the outcome. There is an incentive to optimize the process, to develop the skills. That slow process is a teacher.

Ira Glass talked about this famously too; mastery comes from doing something again and again and again. Until your taste starts to match your skills. When taste and skill are matched, that is what we call ‘craft’. A journalists cranks out piece after piece, a programmer slams the keyboard again and again until it works, gradually building that intuitive understanding of their medium.

taste + skill = craftsmanship

So if craftsmanship used to be about the levelling of combined taste and skill; then how is AI changing this?

What if there is no real effort necessary in creating the artifact? If AI can automate that? How does that change how we learn the necessary skills? But it also opens up possibilities, by sheer volume of explorations available. But here is what I find fascinating: this speed doesn’t necessarily mean we’re learning less. It means we’re learning differently.

How learning is changing

What we’re seeing isn’t the death of learning through practice - it’s the practice evolving into something new. The question isn’t whether we can still get really good at things in this new world. It is whether we’re disciplined enough to learn just as deeply from this completely different process.

Think about what's actually happening when you work thoughtfully with AI-generated iterations. You're not just passively looking at outputs, you're building a new kind of creative muscle. Each variation becomes a case study in *what works and why*. The key word is *thoughtfully*. The hard work isn't in the manual creation anymore. It's in the mental work of evaluating and understanding. The work of keeping your mind with the iteration, not be distracted in the moment, truly be with the AI, to master and create together.

Back to Ira Glass, the equation of craftsmanship still holds. Taste + Skill = craftsmanship. But the actual ‘skill’ is changing. The skill of recognizing, editing and “coaching” the artifact between taste and what’s made, is becoming more important. Not the actual skill of making, but the more meta skill of editing. I believe the craft is shifting from being good at making things to being good at recognizing and refining quality. We're becoming less like the solo craftsperson carefully shaping each piece, and more like a film director working with a talented crew to create something none of them could achieve alone.

This shift demands new kinds of creative skills that are just as challenging as traditional methods:

• Getting Crystal Clear About What You Want: Working effectively with AI requires you to explain your creative goals with incredible precision. You can't be vague and hope the tool will "figure it out." This forces a level of clarity about what you're trying to achieve that many traditional methods never demanded.

• Making Sense of Options Quickly: When you can generate dozens or hundreds of creative options, you need to develop the ability to rapidly identify what works, what doesn't, and *why*. Your capacity to quickly spot quality becomes a core skill, maybe *the* core skill. It's like being a talent scout, but for ideas.

• Developing Sophisticated Taste: In a world where anyone can generate many options, your ability to identify the truly great stuff becomes your main advantage. This isn't just having opinions—it's developing an increasingly sharp sense of what makes something exceptional.

Rethinking the 10.000 hours

Here’s where it gets really interesting. If you commit to deeply understanding each iteration, not just glancing at the surface but genuinely digging into why one version works where another fails, you’re potentially accessing a learning intensity that previous generations couldn’t imagine.

Where a carpenter might first create just a dozen pieces a year, what if you could make hundreds? Learning a little less of each, but in total volume so much more?

That is essentially what we’re having access to now. If we approach learning with the right mindset. The 10,000 hours don’t disappear; they get compressed into a much denser learning experience. The struggle doesn’t vanish, it transforms into a different kind of mental and creative work.

What does that look like? A graphic designer used to spend hours manually adjusting layouts, testing color combinations, tweaking typography. Each change took time and effort. Now they can generate dozens of variations quickly, but the real skill becomes recognizing which variations actually solve the design problem, understanding *why* certain combinations work better, and knowing how to guide the tools toward better solutions. *The learning happens in the analysis and direction, not just in the manual execution.*

For me, this represents an incredible democratization of creative exploration. We're no longer limited by how much physical material we can afford or how much time each experiment takes. We can test ideas that would have been too expensive or time-consuming to attempt before. We can fail faster, learn faster, and work toward excellence at speeds that were previously impossible.

The Discipline of Speed

But crucially, none of this happens automatically. The tools give you the capability; you have to supply the discipline. The risk isn't that AI makes us less creative. It's that we get lazy in how we use it.

We all know the shitty LinkedIn posts that are directly copy-pasted from chatGPT. Zero skill, or taste for that matter, involved. That is not speed, that is laziness. The difference is entirely in how intentional you are. Are you using these tools to avoid the hard work of creative thinking, or to amplify your capacity for that work? Are you letting the tools make your creative decisions for you, or using them to refine and strengthen your own judgment?

The creators who thrive in this new landscape won’t necessarily be those with the strongest traditional skills (though those certainly help). They’ll be the ones who can navigate this expanded creative possibility space with good judgement, refined taste and strategic thinking.

Think about what becomes possible when the friction of creation drops dramatically. A designer can test dozens of approaches to a brand identity. A programmer can prototype multiple solutions to see which one actually works best. A writer can explore different narrative structures for the same story.

The creative landscape ahead looks incredibly exciting to me.

• Platform shifts are fundamentally about new tech becoming reliably available at a predictable, accessible cost; fostering trust and experimentation

• AI is unique in that it constitutes two platform shifts

• The first platform shift is technological; commoditising natural language as a building block. Thus making it easier to integrate with the ‘fuzzy’ real world. Thus holding the potential to transform virtually every service, applications and business.

• The second shift is societal in nature; getting used to machines doing what we previously (proudly) did not imagine a machine capable of doing

“AI is a radical platform shift”. We have been hearing this for a decade. A lot more these last three years. And so much more the last three months.

Yet, with so much talk about ‘shift’, I’m often confused as to what that means. What constitutes a platform shift? Hell, what does ‘platform’ even mean?

Start with the foundation

"Platform" is one of those wonderfully vague words. You can stretch it to fit almost anything – the internet, an OS, an online marketplace like Uber. For me, when I cut through the noise, a ‘platform’ is simply a foundation. It's something stable and predictable enough that you can rely on it to build something else on top. Think of the electric grid: you plug in an appliance trusting the power will be there, consistent and reliable, without a second thought. That’s a platform – a bedrock you can build upon.

Consider the telegram. Not the messaging app, the old-fashioned “analogue” messaging service. This wasn't a single invention but a stack of innovations, big and small, from understanding electromagnetism to the operational know-how of amplifying signals. The telegram itself was built upon the earlier platform shift of widely available electricity. And what did the ‘platform’ of the telegram enable? Messages at virtually zero marginal cost, zipping across the country almost instantly. It famously bankrupted the Pony Express in what felt like a week. More profoundly, these cheap, fast messages supercharged commerce by enabling businesses to coordinate activities across vast distances , accelerated the spread of news which shaped public opinion, and allowed for the management of larger, more complex organizations. It was a catalyst.

The platform shift of the telegram was about the consistently predictable price and availability of service. It became something you could reliably use, a dependable tool at your disposal.

And this is what a platform shift is truly about: economic viability and accessibility, fostering enough trust that others view the new technology as a reliable tool. New tech gets invented all the time. Brilliant ideas often simmer for years. But the shift? That only happens when the cost of using that technology drops, when it moves from the R&D labs and the hands of a few into the grasp of many. It’s when it stops being a precious, prohibitively expensive artifact and starts becoming a tool – something to experiment with, something you can even afford to ‘waste’ a bit, to play around with, without betting the entire farm.

Platform shifts, therefore, are about unleashing experimentation. They mark the transition from "Can I even touch this?" to "What if I could…?". A platform shift in itself doesn’t hold intrinsic value; it holds potential for value creation. It’s like a powerful new drill in a workshop; the excitement isn’t just the drill itself, but what we can now build with it because the technology has become cheap enough, and consistent enough, to take that leap of faith.

The AI shift is different

Which brings me to AI. It feels different. The buzz around this is louder, more persistent. What explains this?

Like any ‘platform’ that came before it – PCs, the internet, cloud, mobile – AI is built on a stack of pre-existing technologies and innovations. It relies on the vast datasets from the internet, our collective digital literacy (we’re all comfortable with chat apps, aren't we?), and countless innovations in machine learning and chip design. And AI, like its predecessors, holds the promise of enabling vastly new tools, services, and businesses.

But that alone doesn’t explain the persistent, almost feverish buzz that has surrounded AI for years, a hum that seems to grow louder by the day. I believe that extraordinary energy comes from the human-ness we instinctively ascribe to current AI, particularly language models. Using ChatGPT feels uncannily human. We see its outputs and attribute to it human-like abilities, creativity, even understanding. And in turn, we affirm our own amazement with what we perceive as its creative power. We are rapidly getting used to a technology that feels human, even though we intellectually know it is not.

It is this anthropomorphism that explains the buzz, I believe. It is the continuing amazement at creativity that fuels inspiration and concern. It is these human-like abilities we don’t quite know how to make sense of. And therefor it is these abilities that keep us circling back to the potential disruption of AI. Especially for us technologists.

It’s a more philosophical fascination, and it’s this abstract quality that can also make the conversation feel a bit unmoored. Because for all the exciting experiments and breathless talk, we are still in the early days of seeing how AI will fundamentally reshape the fabric of our daily lives and work. I, for one, am incredibly excited to find out.

The two platform shifts

And this is why AI, I believe, constitutes two platform shifts rolled into one.

The first shift is purely technical, fitting the classic model. It’s about the underlying technology and the new potential it unlocks. Large Language Models are making language itself a programmable, malleable material. This allows us to bypass traditional complexities in software development – often skipping the need for intricate data integration scripts or complex user interfaces for a whole range of tasks. Because language is so fundamental to nearly everything we do, the applicability of this technical shift is almost boundless, promising disruption and innovation across countless domains.

The second platform shift is driven by this anthropomorphism. Perhaps "paradigm shift" is a more accurate term here, as it’s more about a change in our societal relationship with technology. It’s about us getting used to the idea of machines performing tasks – logical, creative, conversational – that we once considered uniquely human. Remember the Mechanical Turk in the 18th century? Though ultimately a clever hoax, its illusion of a chess-playing automaton captivated imaginations. It wasn't about the reality of that specific machine, but about the idea it sparked – what if machines could reason, strategize, create? That captivation, that speculative wonder, is mirrored today with AI. Our amazement at what AI appears to do fuels an immense creative and speculative energy, even if we're still figuring out its true nature and limits.

It is this second, more paradigm-shifting aspect – our continued amazement at machines doing what we previously thought only humans could – that fuels so much of the intensity in today’s ‘platform shift’ discussions.

Personally, I’m extremely excited about how the ‘technological commoditization’ of natural language via AI can disrupt services, applications, and fundamentally change how we interact with information and systems. But I confess, I am perhaps even more excited by the sheer energy, creativity, and boundless curiosity that this era of increasingly anthropomorphized tech inspires.

Your business shouldn't serve your software. But let's be honest—most of the time, it does.

You've seen it everywhere. Companies hiring expensive consultants to jam their workflows into rigid ERP systems. Teams forced to use interfaces that feel like punishment. Businesses spending months bending their processes around software built for someone else's idea of how work should happen.

This drives me crazy.

I'm a tech strategist. I've built systems, wrestled with architectures, lived in the world of APIs and databases. But I'm also obsessed with strategy, with design, with how we organize our work and empower ourselves.

And here's what I've been watching unfold: tech is finally reaching a point where we can flip this relationship.

The tools that used to require entire engineering teams are becoming accessible to everyone. The rigid architectures that locked us into inflexible systems are giving way to malleable, adaptable platforms. Small businesses are building custom solutions in weekends. Local companies are creating tools that rival what enterprises spend millions on.

This isn't about becoming a software company. It's about becoming the architect of your own efficiency.

I started InContext because this shift deserves more than surface-level trend reports or dense technical papers. It deserves exploration by people who aren't professional developers but who are curious about what's suddenly possible.

For years, I've been obsessed with these questions in the shadows, as a hobby. How do our tools shape our businesses? How can we leverage tech to become more creative, more productive, more empowered in our work? What happens when we stop adapting to our software and start making our software adapt to us?

This is my attempt to open up that conversation.

Every few weeks, I'll explore a specific shift happening in the tech world and translate what it means for your business. We'll look at new tools, emerging patterns, real experiments from people who are quietly discovering what's possible. Sometimes we'll get technical about software architecture—but always in service of practical possibilities.

I'm not here to persuade you to adopt every new trend. I'm here to help you see around corners. To spot opportunities others might miss. To give you permission to experiment with ideas that might seem too ambitious.

Most importantly, I want this to be a conversation. The best insights come from seeing how different people approach the same challenge. I want to hear about your experiments, your frustrations with current tools, your "what if" moments.

Think of this as your translator for what's shifting in tech—and what it means for how you work.

Because the future belongs to businesses that shape their tools, not the other way around.

Welcome to InContext.

—Stijn

P.S. A question for you; What are the three (tech) frustrations in your business you believe there simply must be a better way, but your people assure there isn’t? Let me know, let’s have a chat.

But what remains the slowest operation in computing?

Clicking the button.

Not the literal act of clicking, but what it represents—the moment a human has to think, make a decision, and figure out what needs to be done.

The Human Bottleneck

Computers execute tasks at blinding speeds. But most work isn’t about raw execution. It’s about deciding what to execute.

Writing code isn’t slow because of the compiler—it’s slow because you have to design the logic, debug errors, and make architectural choices.

Spreadsheet analysis isn’t slow because of Excel—it’s slow because you have to think through the numbers, their meaning, and what insights they provide.

Graphic design isn’t slow because of rendering—it’s slow because creativity takes time, iteration, and refinement.

Computers wait for us.

Automation vs. Thinking

If a task is repetitive, we can remove the human. We write scripts. Automate workflows. Let the machine handle the drudgery.

But most of our work isn’t repetitive. It’s one-off.

Every month, a script can reformat sales figures in Excel to generate a report. But what happens when you need to analyze a new dataset, answer an unexpected question, or make a strategic decision? Automation doesn’t help. Thinking is required.

And thinking doesn’t scale exponentially.

The Limits of Speed

AI models like ChatGPT can generate text in seconds, but only after a human crafts the right prompt, evaluates the output, and decides what to refine.

Cloud computing can process terabytes of data, but a human still has to decide which data matters and why the analysis is being done in the first place.

GPUs render stunning game graphics instantly, but designing a compelling game world is a slow, human-driven creative process.

Technology accelerates execution. It doesn’t accelerate thought.

The Real Challenge

No innovation—no AI, no cloud computing, no hardware breakthrough—will remove the fundamental bottleneck of human cognition.

The best we can do is build tools that support thinking, structure decisions, and reduce unnecessary friction. But ultimately, the speed of work is limited not by how fast a machine can execute, but by how fast a human can decide what needs executing.

And that will always be the slowest part of computing.

Uber transformed taxis. Airbnb reshaped hospitality. Social media redefined how we connect. Platforms changed industries, rewrote rules, and sent governments scrambling to catch up.

But is this model still relevant?

It feels like we’ve reached the peak. Everywhere you look, businesses talk about ecosystems and platforms. A model that’s had its moment. Now, a new wave is emerging. One that will change the technological landscape—and possibly leave Europe trailing behind.

Beyond platforms: the shift to integrated tech

Platforms are marketplaces. They bring together supply and demand in clever ways. It’s why Uber disrupted taxis. Why Airbnb keeps reshaping travel. The magic was in how they connected people.

But there’s a limitation. Platforms are transactional by nature. You give something (money, data), and you get something in return (a ride, a stay, a post). The interaction ends there.

Integrated tech moves differently. It’s not about a one-time transaction. It’s about a system where each service feeds into another, creating a loop that continuously improves. A loop that builds on itself.

Look at Apple’s “Intelligence” features. Your iPhone pulls data from Mail, Calendar, Messages. But it doesn’t just store it. It turns that data into context. This context flows across apps—suggesting better texts, smarter reminders. The more you use it, the better it gets.

Each app becomes more useful, not on its own, but because it’s part of a larger whole. The byproduct of one service enhances the others. This is beyond platforms. This is integrated tech.

And when this loop kicks in, the platform era fades out.

The regulatory wall

Here’s the catch: Europe’s regulations.

The Digital Markets Act is built to keep platforms in check. It’s designed for a world where each service is a standalone, where interactions are siloed. It’s all about preventing monopolies and ensuring fairness.

That made sense when platforms ruled. A service started, finished, and was done. But integrated tech breaks that mold. The real value lies in how these services work together.

In Europe, these regulations could choke the integration effect. If data must stay isolated, if apps can’t talk to each other, then the magic of integrated tech fizzles out. No one’s going to manually connect these dots. It has to happen seamlessly, behind the scenes.

Without this integration, Europe risks getting left behind. While other regions push ahead, leveraging data to create smarter services, Europe might stay stuck in yesterday’s game.

Europe’s crossroads

We’re seeing it already.

Apple’s advanced features are missing in Europe. OpenAI is hinting it might pull out. NVIDIA faces pressure to split up under antitrust laws. Europe’s push for fairness is noble, but it’s coming at a cost.

The rest of the world is moving on. Integrated tech is set to create self-reinforcing loops of better services. And Europe? It risks staying in the sandbox, offering good but not great experiences.

A continent that lagged in the social media era now risks missing the next wave altogether.

Finding a new path

Regulation isn’t the enemy. Europe has always led in protecting users, and that matters. But the rules need to evolve.

The focus should shift from isolation to safe integration. There’s a way to protect privacy while still allowing services to enhance each other. To find that balance where innovation thrives without feeding monopolies.

Integrated tech is no longer a distant concept. It’s here. It’s already changing the way we interact with our devices. The question for Europe isn’t if, but how it will adapt.

Will Europe lead this new era—or will it watch from the sidelines?