When the future stops being future and starts being Monday.
Turns out the future has terrible timing. It showed up on a Monday, unannounced, while everyone was still arguing about whether it was coming. Microsoft’s been letting machines write a quarter of their production code for months now, and somehow we’re still having panel discussions about ‘AI’s potential impact.
I’ve been documenting technology revolutions for two decades. I was ringside for the web explosion, social media’s rise, the mobile takeover. But this feels different. This time, the disruption isn’t coming for “other people’s jobs.” It’s coming for us—the knowledge workers, the creative class, the ones who were supposed to be safe.
The silence lasted exactly three seconds—long enough for the math to hit. If Microsoft, with their armies of senior developers and rigorous code review processes, trusts machines to write a quarter of their production code, what does that mean for the rest of us?
The Great Inversion
We got the script backwards. Remember when we told our kids to skip the factory jobs, avoid driving trucks, get into software development or law or consulting? Those safe, intellectual careers that couldn’t be automated?
Turns out the opposite was true. The first casualties aren’t blue-collar workers—they’re the white-collar professionals producing “correct and verifiable answers.” Legal research. Code debugging. Graphic design. Customer service. Marketing copy. The jobs that follow predictable patterns are the ones getting hollowed out first.
Sam Altman dropped this bomb recently: most of OpenAI’s important functions are now AI-written. Not 25% like Microsoft. Most. When the CEO of the company building these tools says AI is handling the majority of his critical work, we’re not talking about gradual adoption anymore. We’re talking about fundamental restructuring of how intellectual work gets done.
This is simultaneously a crisis and it’s an opportunity. The question isn’t whether AI will change your job—it’s whether you’ll be driving that change or getting run over by it.
Voice from the Underground
I discovered this while hiding out on Galiano Island during the pandemic, pretending to be a farmer. A friend visited and showed me Midjourney. Within hours, I’d started a Discord server and invited five collaborators from my 20-year network of tech journalists, artists, and makers.
That server now has 850 members. Not followers—collaborators. People building weird shit together. Testing the boundaries. Breaking things and sharing the pieces.
This is where I learned the real lesson: the tool itself isn’t the revolution. The community using the tool is the revolution.
While everyone else was debating whether AI art was “real art,” we were building AI-human collaboration workflows. While others worried about job displacement, we were discovering new forms of collective intelligence. While the think pieces multiplied, we were shipping actual projects.
The Generational Split
Altman made another observation that’s been haunting me. He’s been watching over people’s shoulders to see how different generations use AI:
- Older folks: Google replacement
- Middle-aged: Life coach and decision advisor
- Twentysomethings: Won’t make major decisions without consulting AI first
- Youngest users: Operating system for life—tying everything together
Each generation down is using AI one level deeper into their cognitive stack. The kids aren’t just asking AI for answers. They’re building AI into their thinking process itself.
I caught myself doing this. Started with simple stuff—rewrite my resume, update my website copy. Now I’ve got AI agents handling transcription, content creation, knowledge synthesis, even social media management. My entire creative workflow runs through AI-augmented processes.
But here’s the kicker: it made me more creative, not less. More human, not more machine-like.
The Knowledge Crystallization Engine
Last month, I built something I call WhisperForge. It’s an AI agent system that takes audio recordings and runs them through a cascade of specialized AI assistants: transcription, wisdom expansion, outline generation, social media content creation, image prompting, and long-form article writing. Then an editor thrashes the whole thing and runs it through again.
It sounds like automation, but it’s actually augmentation. The AI doesn’t replace my thinking—it amplifies it. Takes my raw voice recordings and helps me discover what I was actually trying to say.
At the conference where I gave this keynote, I ran WhisperForge on every single session. By the end of the day, the organizer had a complete knowledge base of synthesis, analysis, and content assets. Six months ago, you would have needed a team of journalists to produce the same output. Now it’s a one-person operation with AI backup.
But the deeper insight hit me later: we can now ask questions that were literally impossible before. “What were the five emerging themes across all speakers?” “How do the AI predictions from the security experts compare with the marketing folks?” “What connections did nobody make explicitly but the data reveals?”
We’re not just automating tasks. We’re creating new categories of cognitive capability.
The Prototype Economy
The development process is changing too. Used to be: client explains what they want, I write requirements, build prototypes, iterate for months, finally ship something that’s hopefully close to what they actually needed.
Now it works like this: while the client is still talking, I’m building functional prototypes in Cursor or V0 or Lovable. By the end of the call, I can show them three different versions of their idea. They can click around, feel the interfaces, understand what they’re actually asking for.
This isn’t just faster iteration. It’s a fundamental shift from specification-driven development to exploration-driven development. Instead of trying to perfectly define requirements upfront, we prototype rapidly and let the tools teach us what we’re building.
The tools have gotten good enough that the bottleneck isn’t implementation anymore—it’s imagination.
Building the Learning Commons
Launching the BC + AI Industry Association has been teaching me the rate of change is too fast for individual learning. You need collective intelligence or you drown in the noise.
Traditional education can’t keep up. Corporate training is stuck in last decade’s paradigms. The only way to stay current is to form learning communities where people dive deep into different domains and share back what they discover.
This is why I’m obsessed with growing community infrastructure. Not networking for networking’s sake, but actual knowledge-sharing ecosystems. Discord servers where people post their latest experiments. Workshops where failures get celebrated as much as successes. Meetups where the agenda is “show us the weird thing you built this week.”
The Vancouver AI community is bout building resilience against technological vertigo. When everything’s changing simultaneously, you need stable social connections to provide context and meaning.
The Personal AI Revolution
Here’s the practical advice nobody’s giving you: stop waiting for someone else to build your AI tools. Build your own.
It starts stupidly simple. Create a custom GPT. Set the system prompt to “you write for me.” Upload your resume as the knowledge base. Boom—you’ve gone from generic AI to personalized intelligence in five minutes.
But then it gets interesting. Add your writing samples so it learns your voice. Include performance reviews so it understands your professional context. Upload transcripts of interviews or presentations. Feed it your text messages if you’re feeling brave.
What you’re building isn’t just a better chatbot. You’re creating an external version of your own intelligence—one that remembers everything, makes connections you missed, and helps you think through problems from angles you might not consider.
I’ve been building this for two years now. My AI assistant can quote my childhood stories, reference conversations from last month, and synthesize insights across projects spanning decades. It has been expanding my cognitive bandwidth.
The Fabrication Framework
The geeks in the audience should know about Daniel Miessler’s Fabric project—a community-maintained, open-source prompting framework that’s quietly becoming the infrastructure for sophisticated AI use.
Instead of struggling to write effective prompts from scratch, you tap into thousands of community-tested, battle-hardened prompts. Some of these are 1,500 lines of structured markdown—basically small programs written in natural language.
Combined with Ollama for running local models, you can build incredibly sophisticated AI systems that run entirely on your own hardware. No cloud dependencies. No data sharing. Just you, your machine, and models that know how to think the way you need them to think.
This is the open-source counter-narrative to the corporate AI story. While everyone’s fighting over who controls the largest models, the real innovation is happening in the spaces between—the tools, frameworks, and communities that make AI actually useful for regular humans.
The Vibe Coding Revolution
But the biggest shift I’m seeing is in how we build software itself. Traditional development—requirements, design, implementation, testing, deployment—is getting compressed into real-time creative collaboration between humans and AI.
I call it vibe coding, and it’s changing everything. Instead of spending weeks writing specifications, you spend hours building functional prototypes. Instead of hiring teams to implement ideas, you collaborate with AI to explore possibilities.
One of my favorite examples: a friend who runs a tech company lines up his junior developers with AI coding tools and has them build experimental features. His senior developers write test suites that define what “working” looks like. The juniors iterate with AI until their code passes all tests. Only then does it get considered for production.
This inverts the traditional hierarchy. Junior developers get to experiment with cutting-edge tools. Senior developers focus on architecture and quality assurance. AI handles the tedious implementation details. Everyone works at the top of their capability stack.
The Community Immune System
What keeps me optimistic isn’t the technology itself—it’s the communities forming around it. People who understand that this transformation is too big and too fast for individual navigation.
The Discord server I started with five friends has become a distributed research lab. People share experiments, debug each other’s prompts, collaborate on projects that wouldn’t exist without collective intelligence. It’s not just social networking—it’s cognitive networking.
We’re accidentally building the infrastructure for post-scarcity creative work. When the marginal cost of content creation approaches zero, what becomes valuable is curation, synthesis, and meaning-making. The ability to ask better questions. The wisdom to know which problems are worth solving.
The communities that figure this out first will have massive advantages. Not just in terms of technical capability, but in terms of resilience, adaptability, and collective sense-making.
The Adaptability Paradox
Here’s what I’ve learned from two years in the AI trenches: the more fluid technology makes the world, the more rigid we become in our thinking. We cling to job titles, industry categories, and skill definitions that are dissolving in real time.
The solution isn’t to resist change—it’s to become change. Stop identifying with your tools and start identifying with your impact. Stop thinking of yourself as a photographer or developer or marketer and start thinking of yourself as someone who solves specific problems using whatever methods work.
Your competitive advantage isn’t your existing skills—it’s your ability to synthesize new capabilities faster than the competition. It’s your willingness to look stupid while learning. It’s your capacity to build bridges between domains that haven’t been connected before.
The Five-Minute Revolution
So here’s my challenge: build an AI assistant today. Not next week. Today. Five minutes, maximum. System prompt: “You think and write like me.” Knowledge base: your resume. That’s it.
Then try this: record five minutes of voice notes tomorrow morning. Dreams, to-do lists, random thoughts, whatever. Transcribe it and feed it to your assistant. Ask it to analyze your patterns and give you insights.
Do this for a week. Watch what happens.
You’re not just building a productivity tool. You’re creating a feedback loop between your conscious and unconscious mind. You’re externalizing your intelligence in ways that make invisible patterns visible.
After a month, you’ll have a knowledge base of your own thinking that you can query, analyze, and build upon. After a year, you’ll have something that feels like collaborative intelligence with your best possible self.
The Real Disruption
The bloodbath everyone’s talking about—mass job displacement, economic upheaval, social chaos—that’s not the real story. The real story is the emergence of human-AI collaborative intelligence.
We’re being invited to become something new. Hybrid intelligences that can think at machine speed with human wisdom. Creative collaborations that produce insights neither human nor AI could generate alone.
The people who figure this out first will shape what comes next. Not the corporations building the largest models or the governments writing the regulations, but the communities building the most interesting experiments.
The revolution is emerging from Discord servers and makerspaces and late-night coding sessions where people are combining tools in ways the original developers never imagined.
The Choice Point
We’re at a historical inflection point. The decisions we make in the next few years about how to integrate AI into human systems will determine whether this technology amplifies human flourishing or accelerates inequality and displacement.
The corporate narrative wants us to be passive consumers of AI services. The open-source narrative wants us to become AI developers. But there’s a third path: becoming AI collaborators. Learning to think with machines in ways that make both human and artificial intelligence more powerful.
It’s about discovering new forms of intelligence that emerge from human-AI collaboration. It’s about building tools that make us more creative, more curious, more connected to each other and to possibilities we couldn’t see before.
The bloodbath is real. But it’s not the end of the story. It’s the compost from which something unprecedented can grow. The question is whether you’ll help build the future where AI adapts to human flourishing.
The choice is binary: spectator or collaborator.
Kris Krüg runs the BC + AI Ecosystem Industry Association, hosts Vancouver’s AI community, and coaches journalists in AI adoption through Google’s News Initiative. He’s been building communities at the intersection of technology and creativity for over two decades, and currently spends his time helping people navigate the transformation from human-only to human-AI collaborative work.
Discover more from Kris Krüg | Generative AI Tools & Techniques
Subscribe to get the latest posts sent to your email.