While executives debate whether AI will replace programmers, they’re missing the real story: We’ve built the most sophisticated mutualization of our collective intelligence by way of tokenization and resulting weights.
web 1 → infinite read
web 2 → infinite read-write
web 3 → infinite read-write-own
ai 1 → infinite pastebin
ai 2 → infinite remix
ai 3 → infinite agency?
The Paste Machine Discovery
Google helped us search through the global help desk, YouTube helped us learn from others, and now LLMs help us skip these intermediate steps to get directly to customized solutions.
GitHub gave us access to code, but you had to understand it, adapt it, debug it. Stack Overflow was paste + comments and you had to parse through the wrong answers and still figure out if the useful comments applied to your situation. The Great Command-V Machine not only pastes but it tailors along the way. It takes that authentication pattern from 2019, that payment flow from a Berlin startup, that error handling from Python’s style guide, and seamlessly reformats it all into exactly what you need, in your language, with your variable names, for your specific context.
Every response is custom-tailored plagiarism. And I mean that as the highest compliment. One of the benefits of solving similar problems many times over a certain period of time is that you have your own reusable snippets that you understand, that you worked on, and that can solve different parts of your stack’s problems. Need authentication, you got it. Need payments, you got it. Need caching, you got it. Need CRUD, you got it. This used to all be on your local machine. And the most valuable bits you wouldn’t necessarily share directly on GitHub. Only open-source projects would do that.
Now, for a monthly subscription, you get access to everyone’s most valuable paste bin highlights. Not just everyone now, everyone ever.
The competitive advantage of accumulated cleverness, the institutional knowledge that took years to build, that got flattened overnight. Not by making machines smart, but by giving everyone efficient access to all previous smartness. That complex integration pattern your team spent months perfecting? It’s now part of the universal clipboard, available to any competitor with an API key.
When Paste Becomes Strategic Intelligence
When you have infinite paste with perfect contextual awareness, something changes. Projects that were previously too onerous to build are now manageable. And when you stack these up, you end up giving every knowledge worker wings.
It’s like building logic gates from transistors. A single transistor isn’t thinking — it’s just switching current. But stack enough of them in the right patterns, and you get computation. Stack enough computation, and you get something that looks remarkably like thought.
The LLM does something similar with the combination of quickly retrieving solutions, combining them quickly and the ability to iterate quickly with agentic loops in ways that creates new solutions. It still sucks at true “reasoning” through a problem, but its pattern-matching and mimicry at scale becomes functionally equivalent to reasoning. That gives us a decade worth of work imo.
Here’s what matters for business: The machine can search any solution humanity has ever found, customize it perfectly for your specific needs, and combine solutions in ways that create new ones. Whether you call it intelligence or sophisticated paste is somewhat irrelevant - it’s not perfect and AGI will take some time so you better get to work with what you have.
The New Strategic Framework: Paste vs. Intelligence Tasks
Companies need a clear framework for AI deployment. Based on patterns emerging from early enterprise adoption, three categories have become clear:
Paste-Optimal Tasks
These are where AI excels and should be deployed immediately:
- Integration work: Connecting APIs, handling authentication flows, payment processing
- Code adaptation: Taking existing solutions and customizing for new contexts
- Pattern application: Implementing known architectural patterns with company-specific requirements
- Boilerplate generation: Creating repetitive code structures with proper error handling
Organizations report that integration development that previously took senior engineers weeks can now be completed by junior developers in days when AI handles the boilerplate and adaptation work.
Intelligence-Required Tasks
These still need human reasoning and shouldn’t be fully automated:
- Novel problem solving: First-time challenges with no existing patterns
- Strategic architecture decisions: System design trade-offs with long-term implications
- Edge case handling: Unusual scenarios that require judgment calls
- Business logic definition: Domain-specific rules that require deep context
When companies build new multi-tenant isolation systems or design novel data architectures, the strategic decisions around partitioning and boundaries require deep technical judgment. But implementing those designs? That’s paste-optimal work now.
Hybrid Zones
The most interesting opportunities combine paste and intelligence:
- Rapid prototyping: AI handles boilerplate, humans design novel interactions
- Code review: AI catches common issues, humans evaluate architectural decisions
- Documentation: AI drafts technical docs, humans ensure strategic clarity
- Testing: AI generates test cases, humans design test strategy
The companies winning with AI aren’t replacing humans—they’re optimizing this division of labor.
What Happens to Software Itself
Traditional software becomes the substrate that gets remixed. Every app, every feature, every interaction pattern ever built becomes part of the paste buffer. Users won’t need to know where the paste came from.
Major platforms understand this. Salesforce’s AgentForce isn’t trying to replace salespeople—it’s giving every salesperson access to the collective knowledge of every top performer who ever closed a deal. Same with ServiceNow’s workflow automation and Microsoft’s Copilot integrations. They’re building paste machines for domain-specific expertise.
The boundaries between applications dissolve when any capability can be summoned and reshaped on demand. But someone still needs to create the original patterns that get pasted. Someone still needs to push the boundaries, create the new solutions that tomorrow’s AI will remix.
What Remains Human
This is why AI hasn’t made programming obsolete, just different. The truly novel problems—the ones nobody has solved before—still require that grinding, incremental push forward. AI can instantly get you to where humanity left off yesterday, but that last mile? That’s still on us.
The unsolved edge cases remain human territory. When teams tackle alignment problems, solve perception challenges in autonomous systems, or figure out new optimization algorithms—these aren’t paste problems. They’re intelligence problems. The Great Command-V Machine gives everyone access to solved problems. Unsolved problems still require actual intelligence.
The difference is that now, those innovations immediately become part of humanity’s shared clipboard. Every breakthrough gets mutualized instantly. The competitive window for novel solutions is shrinking, but the returns for being first are increasing.
The Strategic Reality
Companies built on accumulated knowledge advantages—consulting firms, system integrators, enterprise software vendors—are finding their moats eroding. The knowledge that justified premium pricing is now available to everyone. New moats are emerging:
Speed of implementation: Getting from concept to deployment faster than competitors. Teams that embrace AI-assisted development report dramatic reductions in time-to-market for standard features, freeing resources for novel work.
Quality of judgment: Making better decisions about which patterns to apply. Product teams that combine AI generation with human curation achieve both speed and quality improvements, rather than trading one for the other.
Novel solution creation: Being first to solve new problems that create new paste. The companies developing new training methods, safety practices, and architectural patterns see their innovations quickly become industry standards.
Hybrid orchestration: Optimizing the human-AI workflow better than anyone else. Engineering organizations that clearly define which tasks go to which intelligence type report the highest productivity gains.
The companies that recognize AI as an infinite bespoke paste machine—and structure their operations accordingly—are seeing measurable advantages. The companies still debating whether it’s “really intelligent” are falling behind.
It’s mutualizing the clipboard that changes everything
We built something remarkable: infinite, bespoke, magic paste that can instantly retrieve any solution humanity has ever found and reshape it perfectly for any context. It can combine these solutions in ways that appear creative. It can pattern-match through reasoning that feels real.
For most of us, the question isn’t whether it’s capable of thinking from first principles. The question should be: What can you build when you have access to this much accumulated knowledge, with the ability to recombine and customize it for our context?
As every recorded solution becomes accessible to everyone. Every innovation gets incorporated into the shared corpus. The knowledge asymmetries that defined competitive advantage are compressing, while new advantages emerge for those who adapt their workflows and business models accordingly. The people who recognize this shift and restructure around it will have significant practical advantages. Those still debating the philosophical questions will likely fall behind.
And those who have thrown up their hands over AGI and the end of knowledge work will find themselves embarrassed when their friends made all the money and are still implementing AI enabled solutions in 2031.