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.
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.