The Regurgitation Paradox: When AI's Greatest Strength Is Its Biggest Weakness
Anders Hejlsberg doesn't mince words. The creator of TypeScript, C#, and Turbo Pascal—someone who's literally shaped how millions of developers write code—recently dropped a bombshell that's been echoing through programming communities. "AI is a big regurgitator of stuff someone else has done." Ouch. But here's the twist: he still believes it's fundamentally changing how software gets built.
This tension between skepticism and optimism captures exactly where we are in 2026. We're using AI tools daily—GitHub Copilot, Cursor, Claude Code—but there's this nagging feeling. Are we building something new, or just rearranging what already exists? I've been testing these tools for years now, and Hejlsberg's comment hits home. The other day, I asked an AI to generate a complex data transformation pipeline. It gave me something that worked perfectly... and looked suspiciously like three different Stack Overflow answers stitched together.
But here's what most discussions miss: regurgitation isn't necessarily bad. Think about how humans learn. We study existing patterns, internalize them, then apply them to new problems. The difference? Humans eventually synthesize something genuinely novel. The question for 2026 isn't whether AI regurgitates—it's whether we can build tools that help it transcend that limitation.
Why Hejlsberg's Perspective Matters (More Than You Think)
When someone with Hejlsberg's track record speaks about programming tools, you listen. This is the guy who looked at JavaScript's wild west and said, "We can do better"—then gave us TypeScript. His criticism carries weight because he understands tooling at a fundamental level. He's not some academic theorizing about AI; he's spent decades building tools developers actually use.
The Reddit discussion around his comments revealed something interesting. Developers aren't worried about AI replacing them tomorrow. They're worried about something subtler: the erosion of deep understanding. One commenter put it perfectly: "I used Copilot to write a React component yesterday. It worked, but I have no idea why the state management pattern it chose is appropriate." That's the real concern. When AI becomes our default problem-solver, do we risk becoming technicians rather than engineers?
From what I've seen in code reviews this past year, we're already seeing this play out. Junior developers using AI tools often produce code that looks competent superficially but lacks architectural coherence. It's like they've been given a phrasebook instead of learning the language. They can ask for directions to the bathroom, but they can't have a meaningful conversation about where to build the hotel.
The Tooling Revolution That's Already Happening
Hejlsberg gets this part absolutely right. AI is reshaping our tools in ways we're just beginning to understand. It's not about AI writing entire applications (though it can help with boilerplate). It's about AI augmenting every part of the development workflow.
Take debugging. In 2026, I'm using tools that don't just point out errors—they explain why the error matters in my specific context. Yesterday, I had a type error in a complex generic function. The AI didn't just say "Type 'string' is not assignable to type 'number'." It said: "You're trying to pass the user's email here, but this function expects their ID. Based on similar patterns in your codebase, you probably want to fetch the ID first from the users service." That's transformative.
Or consider documentation. We all hate writing it, but AI tools now can generate surprisingly good documentation from code—and more importantly, keep it updated as the code changes. I've been using one that watches my commits and automatically updates relevant documentation sections. It's not perfect, but it's getting scarily good.
The real shift? We're moving from tools that help us write code to tools that help us think about systems. That's where the revolution lives.
The Originality Problem: Can AI Create or Just Recombine?
This is the heart of Hejlsberg's "regurgitator" criticism. Current AI models work by predicting what comes next based on patterns they've seen. That's fantastic for generating code that looks like existing code. But what about truly novel solutions?
I tested this recently with a particularly gnarly performance problem. The AI suggestions were all variations of standard optimization techniques: memoization, lazy loading, debouncing. Good stuff, but nothing groundbreaking. Then I paired with a senior engineer who'd been working in this domain for 15 years. She suggested an architectural approach I'd never seen before—one that wasn't in any of the training data because it was genuinely novel.
Here's the uncomfortable truth: most programming doesn't require groundbreaking novelty. 80% of what we build is applying known patterns to specific business problems. For that 80%, AI is already incredibly useful. It's the 20%—the truly hard, novel problems—where we still need human creativity.
The danger isn't that AI can't do the 20%. It's that we might stop training developers to handle that 20% because AI handles the 80% so well. That's a skillset erosion we can't afford.
Practical Strategies for 2026: Using AI Without Losing Your Edge
So how do we navigate this in 2026? How do we use these powerful tools without becoming dependent on them? Based on my experience working with teams adopting AI tools, here's what actually works.
First, treat AI like a brilliant but inexperienced junior developer. It can produce lots of code quickly, but you need to review everything critically. Don't just accept its solutions—understand them. When it suggests a pattern, ask yourself: "Why this pattern? What are the tradeoffs?" If you don't know, research until you do.
Second, use AI for exploration, not just implementation. One of my favorite techniques is what I call "solution brainstorming." I'll describe a problem to the AI and ask for five completely different approaches. Not to use any of them directly, but to spark my own thinking. Often, the fifth suggestion—while flawed—contains a seed of an idea I wouldn't have considered.
Third, maintain your fundamentals. This sounds obvious, but I've seen developers let their debugging skills atrophy because AI finds bugs so quickly. Make yourself debug without AI at least once a week. Keep reading code—real, human-written code from open source projects. The Programming Books 2026 section still has gems that AI won't recommend because they're not the latest trends.
Fourth, customize your tools. The off-the-shelf AI coding assistants are trained on everyone's code. Fine-tune them on your codebase, your patterns, your business domain. This is where tools really start to shine. When the AI understands that your team always uses functional patterns with React, or that your API layer has specific error handling requirements, its suggestions go from generic to genuinely helpful.
The Future Toolchain: What Comes After Autocomplete?
If current AI tools are about autocomplete on steroids, what's next? Based on where the research is heading and conversations with tool builders, I see three major shifts coming.
Context-aware development environments will become standard. Your IDE won't just know the code you're writing—it'll understand the ticket you're working on, the recent changes in related modules, even the discussion in your team's Slack channel about this feature. It'll proactively suggest: "Hey, I notice you're implementing user permissions. Maria refactored the permission service last week—you should check her changes."
AI-powered architecture validation is another frontier. Tools that can look at your proposed system design and say: "This looks similar to the inventory management system you built last year, which had scaling issues at 10,000 requests per second. Here's where this design might break." That's moving from syntax to semantics, from code to architecture.
And then there's what I call "reverse engineering assistance." We've all inherited legacy code that's poorly documented. Future tools will let you point at a complex module and ask: "What does this actually do, and how does it connect to the billing system?" The AI will trace dependencies, analyze data flows, and give you a coherent explanation.
These tools won't replace understanding—they'll demand deeper understanding. You'll need to know enough to ask the right questions and evaluate the answers.
Common Pitfalls (And How to Avoid Them)
Let's get practical about where teams stumble with AI tools. I've consulted with dozens of engineering organizations on this transition, and the patterns are clear.
The copy-paste trap is the most common. Developers get an AI suggestion, it looks good, they paste it in. Two weeks later, no one understands that part of the codebase. The fix? Implement a simple rule: any AI-generated code over 10 lines needs a comment explaining why this approach was chosen. Not what it does—why it's there.
Skill stagnation sneaks up on you. Your team starts relying on AI for database optimizations, and suddenly no one remembers how to read query execution plans. Schedule regular "fundamentals Fridays" where you solve problems without AI assistance. Keep those muscles strong.
The homogeneity risk is subtler. If everyone on your team uses the same AI tools trained on the same data, your codebase starts converging on the same patterns. That's not always bad, but it can limit innovation. Encourage developers to occasionally use different tools or approaches. Diversity of thought still matters.
Security blindspots are real. AI tools suggest code that works, not code that's secure. They'll give you a quick authentication implementation that doesn't include rate limiting or proper session handling. You need security reviews more than ever, not less.
And finally, there's the velocity illusion. Yes, AI lets you produce code faster. But software development isn't just about typing speed—it's about thinking, designing, testing, maintaining. I've seen teams double their "code output" while actually slowing feature delivery because they're generating more code that needs more debugging.
What Hejlsberg Gets Right About the Human Element
Here's where I think Hejlsberg's perspective is most valuable. Amid all the AI hype, he keeps bringing it back to the human developer. Tools should serve people, not the other way around.
The best AI tools in 2026 aren't the ones that write the most code—they're the ones that make developers feel more capable, more creative, more in control. They're tools that handle the tedious parts so we can focus on the interesting parts. They're tools that explain their reasoning so we learn as we go.
I was talking to a startup founder recently who said something that stuck with me: "We don't hire developers to write code. We hire them to solve business problems. Code is just one way they do that." AI tools that help developers understand business context, that connect technical decisions to business outcomes—that's where the real value is.
And this brings us back to the regurgitation criticism. When AI simply rehashes existing solutions, it's solving the wrong problem. The real challenge in software development isn't "How do I implement a login system?" It's "How do I implement a login system that works for our specific users, integrates with our legacy auth service, and scales with our growth trajectory?" That requires synthesis, not just repetition.
Your 2026 Action Plan
So where does this leave us as developers in 2026? First, embrace the tools—they're not going away, and they genuinely help. But embrace them with clear eyes. Understand their limitations as well as their capabilities.
Second, focus on what makes you human. Your domain knowledge, your understanding of your users, your ability to connect technical decisions to business outcomes. AI can't do that—not yet, anyway. Those are your competitive advantages.
Third, keep learning—but learn differently. Instead of memorizing syntax, learn how to evaluate AI suggestions. Instead of studying frameworks, study problem-solving patterns. The find mentors who can guide you through this transition—sometimes an hour with someone who's been through it is worth weeks of trial and error.
Fourth, contribute to the conversation. The tools are evolving rapidly, and developer feedback shapes that evolution. When you find a pattern that works, share it. When you hit a limitation, report it. We're building this future together.
Hejlsberg called AI a "big regurgitator," but he also sees it changing everything. That tension—between skepticism and optimism, between human creativity and machine efficiency—is exactly where progress happens. The developers who thrive in 2026 won't be the ones who reject AI tools or blindly accept them. They'll be the ones who understand both their power and their limitations, who use them to amplify their own skills rather than replace them.
After all, that's what good tools have always done. From the first compiler to the first IDE to TypeScript itself—they don't replace the developer. They make the developer more capable. The AI tools that will matter in 2027 and beyond will be the ones that remember that fundamental truth.
