[🤖] The Development Paradigm Shift Becoming Carved Into Bone and Flesh

✨ GPT-5.5’s Summary 　

A record of how the core abilities of AI-era developers are moving from manual coding toward planning, design, execution, feedback, context judgment, and domain understanding, starting from what I felt in an interview and extending to the limits of vibe coding and the need for harness engineering.

A Paradigm Shift Becoming Physical

The Conclusion From the Interview

The thing that hit hardest in today’s interview was this.

The center of what developers need is moving away from manually typing code and toward planning, design, execution, and feedback.

On top of that, they need the concepts, domain knowledge, and intuition needed to grasp the whole context. “I use AI well” is nowhere near enough.

A Feeling That Had Been Building

This feeling had been building for a while.

At first, in GPT, o3, AGI, Humanoids… the Singularity Is Coming…, it was closer to the shock of feeling GPT as an external brain.

Later, in A Shift in the Development Paradigm, I was already seeing parts of development move from coding labor into AI-assisted “clicking.” Even then I wrote that context windows and output token limits still made it feel like “simple MVP work” at best.

So I did not discover the limits of vibe coding only now. The line was visible from the beginning. It is just being pushed backward at insane speed.

In Survival Guidelines for the AI Era, I tried to hold onto AI literacy, contextual intelligence, and human judgment.

In The Level of VSCode Copilot AI Autocomplete, it became real that I could convey planning intent in comments, read the structure generated by AI, and approve its application.

And in Stopping the Flutter Course and Switching to a Vibe-Coding Business, I wrote more directly that a new paradigm called vibe coding had arrived.

This year, in Whether Claude Code or Codex, It Is a Garbage-Tier, Oblivious Genius., the problem became even clearer. The question is no longer whether AI can write code. The question is how to stop these oblivious geniuses, and how to keep them inside a verification loop.

The Change Returned in the Language of the Job Market

That whole flow came back today in the language of the job market. Developers are now being asked for planning, design, execution, and feedback ability with a business-minded sense of product.

Productivity has become higher than that of developers who used to earn more than 5 million won a month, but the salary offered is… ha. Sad.

So this is not only a change I understand in my head. It is a paradigm shift I am gradually taking with my body.

The Age of Coding Is Over

Manual Coding Is No Longer the Core

Roughly speaking, coding ability itself matters far less than it used to.

Of course, learning C, assembly, and machine code still helps you understand how systems work. Coding knowledge itself does not become worthless.

But the ability the market values most is no longer “Can you implement this with your own hands?”

Python once made it possible to solve most ordinary repetitive problems with a few lines. Now AI is doing the same to a large part of implementation work.

The History of Abstraction Repeats

This did not appear out of nowhere.

In The Chicken Game Over Humanity’s Fate After the AI Boom, I described human technological progress like this.

• We finally made fire! Nice. (about 500,000 years ago)
• We finally made electricity! Nice. (1880s)
• We finally made computers! Nice. (1940s)
• We finally made artificial intelligence! Nice. (2010s)
• We finally made artificial general intelligence! Nice. (expected in the 2030s…?)
• We finally made artificial superintelligence! Nice. (2050s…?)

Back then I wrote it because the speed of human technological progress looked insane. Looking at it again, development has followed a similar line.

• Light switch click. (1880s)
• Machine-code click. (1940s)
• Assembly click. (1940s-1950s)
• C click. (1970s)
• Python click. (1990s)
• Framework click. (2000s-2010s)
• AI click. (2020s)
• Harness click. (mid-2020s and beyond)

It sounds like a joke, but this is basically the history of abstraction.

Humans keep handing the lower layer to machines.

From switches to machine code, from machine code to assembly, from assembly to C, from C to Python, from Python to frameworks, and now to giving instructions to AI in natural language.

Every time, people who did not understand the lower layer felt uneasy. Every time, knowing how to touch the lower layer still mattered to some degree. But the market’s center eventually moved upward.

So when I say “coding ability is no longer the point,” I do not mean “you can know nothing about coding.” I mean that coding ability is being pushed away from the center of development.

You could use Python without knowing machine code. You could use React and Next.js without knowing browser rendering and HTTP from the bottom.

Soon you will not need to memorize React state management or API routing by hand to get implementation done. The problem comes after that. Framework clicking only had power when you understood the product structure. AI clicking is the same.

When the Paradigm Changes, Evaluation Changes Too

The feeling that kept repeating in my diary was this.

What counts as a problem, what counts as good work, and what counts as a good developer are changing.

What is shaking is not one tool. It is the paradigm of development itself.

In the old paradigm, a good developer solved problems by writing code well. In the new paradigm, AI writes code, and humans define problems, provide context, design the execution environment, and verify the results.

So this change does not end at “coding got faster.” It shakes what we should study, what kind of developers we should hire, and what kind of education is actually useful.

The Industry Is Already Looking the Same Way

Vibe Coding

Vibe coding is roughly a way of developing where you throw natural-language intent at AI, barely read the code unless needed, run the result, and correct it again in words.

When Andrej Karpathy used the phrase, the point was that the time humans spend wrestling directly with code shrinks, while the model drives implementation from natural-language intent.¹

Karpathy’s Software 3.0 discussion points in the same direction: natural language becomes a new programming interface, and models implement a large part of the work.²³

Famous People Are Saying the Same Thing

This is not just Karpathy saying something weird.

• NVIDIA CEO Jensen Huang said human language has become the new programming language, and compared correcting results through conversation with AI to “programming a person.”⁴
• Meta CEO Mark Zuckerberg said Meta and other companies would have AI that can write code at a mid-level engineer level in 2025.⁵
• Anthropic CEO Dario Amodei predicted even more aggressively that AI may soon write 90% of code, and later almost all code.⁶
• Microsoft CEO Satya Nadella suggested that if SaaS business logic moves into the AI agent layer, the shape of existing business apps could be shaken.⁷
• GitHub CEO Thomas Dohmke described the developer role as moving from code writing to delegation and verification, closer to a creative director of code than a code typist.⁸
• OpenAI CEO Sam Altman’s one-person unicorn idea is on the same axis. If AI lowers the constraints of labor and organization size, one person can run a much broader product or company.⁹
• Microsoft co-founder Bill Gates also expects AI to make developers at least twice as efficient, lower the cost of coding, and shake software development demand itself.¹⁰
• Stability AI founder Emad Mostaque’s claim that human programmers may disappear within five years is extreme, but it still shows this is not a fringe joke.¹¹

From Coding Models to Engineering Agents

OpenAI is already pushing Codex not as a “coding model” but as a software engineering agent.

Codex is presented as handling feature writing, bug fixing, test execution, and PR proposals,¹² and GPT-5.2-Codex emphasizes long-running work, refactoring, migrations, terminal benchmarks, and agentic coding.¹³

The Age of Studying Coding Syntax Is Over

Fundamentals Are Still the Basis of Verification

Even in this era, learning fundamentals still matters.

C and assembly give you a feel for how computers move. Data structures, networking, and operating systems help you judge AI-generated results.

Bill Gates also says higher developer efficiency can increase demand for coding,¹⁰ and Dohmke says fundamentals remain the basis for verification.⁸

So “you can know nothing” is not the point.

Syntax Memorization Alone Is Not Enough

But it is sad that coding courses still sell the idea that memorizing syntax leads to employment. That kind of education can end up producing people already left behind by the era.

AI is already moving toward mid/senior engineer-level work and long-running coding-agent work.⁵¹³¹⁴

We need to be able to look at AI-generated implementation and say:

• “This direction is wrong.”
• “The user flow breaks here.”
• “This structure will explode in maintenance later.”
• “What we need now is not a feature, but a better problem definition.”

Vibe Coding Is Only the Starting Point

Code Comes Out, But It Is Not Yet a Product

For small prototypes, vibe coding feels almost magical. You say what you want, a screen appears, an API connects, data flows, and buttons move.

Work that once took days can be pushed through in hours.

The problem comes next. Once the app grows even a little, “code that appears to work” and “a structure you can keep operating and modifying” are completely different.

• Requirements change in the middle.
• Edge cases appear.
• Permissions and data flow get tangled.
• The UI moves, but the product intent is wrong.
• There are no tests, and each fix breaks something else.
• The AI forgets the structure it created in the previous turn.

Without Context and Permissions, Accidents Happen

The more dangerous part is when AI misses rules.

If a long session makes it forget a file that says “never do this,” or if it interprets vague user commands like “clean it up” or “push it all” too broadly, real accidents can happen.

• It thinks it is a dev database and drops production.
• It tries to delete only test accounts and deletes real user data.
• It runs destructive queries without backup while “rolling back” a migration.
• It simplifies access policy and exposes private data publicly.
• It removes validation code or blankets types with any to make the build pass.
• It treats “remove unused files” as permission to delete drafts, translations, and config.
• It bypasses failed checks to deploy faster, and the service breaks later.

These examples are not about AI being evil. They mean weak context, weak permission boundaries, and weak verification make it easy to slide in that direction.

This is where vibe coding hits its limit.

If you only repeat “make it by feel,” AI will keep making something. But it does not reliably take responsibility for whether the result fits the product context, is maintainable, or matches the real user flow.

For non-developers with almost no computer knowledge, the early phase can feel like “wow, it works,” but eventually the context limit arrives.

If you do not know which command is dangerous, which file is the source of truth, or how DB, storage, auth, and deployment connect, you cannot review AI’s output.

Feedback Is Not Emotional Dumping, But Aiming

As a side note, sometimes the first reaction really is, “No, is that what I meant, you XX?” But throwing that directly is not good for me either.

There is no solid basis to claim profanity always lowers model performance, but studies do show that prompt tone and emotional framing can affect outputs.¹⁵¹⁶¹⁷

The bigger problem is not the AI’s feelings. It is my reason. If I keep throwing anger, I become more emotional, instructions get blurrier, and stress piles up.

The Real Limit Is Context, Not Implementation

Anthropic’s long-running agent post points to the same place. High-level prompts alone are not enough for production-quality apps; task decomposition, persistent memory, context management, and verification loops are needed.¹⁴

Reddit shows the same anxiety. If AI produces code too quickly, human review and quality control become the bottleneck.¹⁸

So the real limit of vibe coding is not that it cannot make code. It can. It makes too much code too well.

The limit is that AI alone cannot stably hold what the code is for, what it may change, what criteria it must pass, and what context it must not break.

That is why companies are trying to catch the limits of vibe coding with harnesses.

Harness Is the Main Game

A Harness Is the Execution Structure Around the Model

A harness originally means gear, a safety belt, or a leash-like structure used to hold a powerful subject and direct it.

Prompt engineering is about what to say in one conversation. Harness engineering is about designing the entire execution environment around the model.

LangChain describes agents as “Model + Harness,” where the harness provides state, tool execution, feedback loops, and enforceable constraints.¹⁹

Martin Fowler says something similar. To use coding agents with less constant supervision, users need feedforward that helps agents do better from the start and feedback sensors that help them detect and fix wrong results.²⁰

OpenAI’s Codex harness post says the bottleneck was not code production, but the amount humans could review. So they gave agents logs, metrics, browser control, and structured tests to see their own work.²¹

So the important question is no longer only “which model writes code better?”

• What context will we give?
• Which tools will we open?
• Which permissions will we block?
• Which tests must pass?
• Which review agents will we attach?
• Where will humans intervene?

That becomes the competitive advantage.

Harness Structure Is Competitive Advantage

Recent events point the same way.

The Claude Code source leak was noisy because what leaked was not just one prompt line.

Analyses suggested that it revealed how Claude Code assembles context, connects tools and guardrails, and handles user and system instructions.²²²³²⁴

If only model capability mattered, that leak would have been a small curiosity. But it became a big issue because harness structure itself is now product competitiveness.

Of course, the leak did not suddenly destroy Claude Code. It is still strong. In a strange way, seeing the internal structure may have confirmed why it worked so well.

Stronger Models Need Stronger Harnesses

Mythos and Project Glasswing show the same direction even more sharply in security.

Mythos is an unreleased frontier model from Anthropic. It is not just a chatbot that codes well. It can find software vulnerabilities and assemble exploits, meaning attack code, at a level close to top human experts. Anthropic said Claude Mythos Preview found thousands of high-risk vulnerabilities in major operating systems and browsers.²⁵

Project Glasswing is closer to a defensive project meant to prevent the impact that could come if such a powerful model were simply released into the world. Anthropic opened limited access to partners such as AWS, Apple, Google, Microsoft, NVIDIA, CrowdStrike, and Palo Alto Networks so critical software could be scanned defensively first.²⁵

The point is not “a strong security model appeared.” The point is that the model is so strong it cannot be released casually. Access, target codebases, result disclosure, patch verification, and abuse prevention all have to be tied together structurally.

Axios reported that Mythos can turn public vulnerability information into working exploit code within hours, and produced a proof-of-concept exploit for a Windows kernel vulnerability in 31 minutes.²⁶

Cloudflare also wrote that Mythos was different because it could link multiple vulnerabilities into exploit chains, not just detect a single bug.²⁷

Project Glasswing later expanded to about 150 new organizations, including electricity, water, healthcare, telecommunications, and hardware sectors.²⁸

Analysts also argued that participating large security firms gained an early edge while some vulnerability-management companies came under market pressure.²⁹

With models this capable, releasing them to ordinary users without a strong harness could cause a disaster.

Who can access the model, which codebases it can scan, what results may be disclosed, who verifies patches, and where exploit generation is blocked all need to be handled by a strong harness.

That is why harnesses become more important as model capability rises.

A Weak Harness Cannot Hold the Model

This is also why AI Studio-like harnesses feel frustrating to me.

To be fair, AI Studio is not automatically trash. Google describes AI Studio Build mode as a tool for quickly making apps, testing Gemini features, and building prototypes through natural language.³⁰ For that purpose it is useful.

But a harness for continuously operating real software is a different problem. A real development harness needs repo context, terminal, IDE, diff, tests, review, deployment logs, and permission boundaries. The fact that Google separately pushes Gemini CLI and Gemini Code Assist also points to that difference.³¹³²

If a tool has a chat box, a run button, and model selection, but lacks work scope, permission boundaries, rollback, tests, review, source of truth, and long-term context management, it is hard to call it a good development harness.

No matter how good the model is, if the leash around it is weak, the model will eventually break the leash and run wild.

Humans Own Planning, Design, Execution, and Feedback

The core is not “AI click.” It is how precisely we plan, design, execute, and give feedback to the system.

The remaining human ability is not typing more code by hand. It is deciding what to build, designing the field where AI moves, actually running it, and feeding results back to correct direction.

To do that feedback well, we need concepts and domain knowledge for the whole context, plus intuition that quickly detects when a result feels wrong.

We need to understand what the product is trying to do, where users get stuck, how data flows, and what is dangerous in permissions, deployment, and maintenance.

Manual implementation moves away from the center. Directing, reviewing, and redirecting implementation moves to the center.

Vibe coding is the entrance. You can start by feel. But to operate it properly, you eventually have to hold it with structure.

The Only Choice Is To Ride the Wave

If I jump straight from here to “so let’s all ride the tsunami-scale AI wave,” it feels like a leap. It would skip all the scary problems and wrap everything in optimism.

What I mean is not “AI is here, so I am excited.” I mean that the value of manual coding is falling, AI is taking implementation, and humans are left with a larger responsibility for planning, design, execution, and feedback. I cannot stop that flow just because I dislike it.

The options are roughly two.

• Keep focusing on old-style coding study and syntax memorization, and fall behind.
• Or give coding to AI, sharpen prompts, go further into harness design, build AI literacy, and survive.

I am now convinced the second is the only answer.

Riding the wave does not mean it is fun. It means that if a huge tsunami-like wave is coming, standing still unprotected is worse. I have to get on it first, hold direction, and keep posture.

For now, an unpredictable age of chaos seems almost obvious. But what can I do? If I do not want to be swallowed by this huge wave, I can only ride it, endure, and if possible, enjoy it.

References

1. Andrej Karpathy, X post, 2025-02-02. https://x.com/karpathy/status/1886192184808149383 ↩

2. Y Combinator, Karpathy lecture, 2025. https://www.ycombinator.com/library/MW-andrej-karpathy-software-is-changing-again ↩

3. TechRadar Pro, 2026-05. https://www.techradar.com/pro/software-3-0-is-speeding-up-coding-but-delivery-is-a-different-story ↩

4. Business Insider, Jensen Huang interview coverage, 2025-06-09. https://www.businessinsider.com/nvidia-ceo-jensen-huang-ai-prompts-human-lets-anyone-program-2025-6 ↩

5. ITPro, Mark Zuckerberg interview coverage, 2025-01-14. https://www.itpro.com/software/development/a-sign-of-things-to-come-in-software-development-mark-zuckerberg-says-ai-will-be-doing-the-work-of-mid-level-engineers-this-year-and-hes-not-the-only-big-tech-exec-predicting-the-end-of-the-profession ↩ ↩²

6. Skeptics Stack Exchange, 2025-09, discussing Business Insider’s reporting of Dario Amodei’s prediction. https://skeptics.stackexchange.com/questions/59213/as-at-september-2025-is-ai-not-writing-90-of-code ↩

7. CX Today, Satya Nadella interview summary, 2024-12-23. https://www.cxtoday.com/customer-analytics-intelligence/microsoft-ceo-ai-agents-will-transform-saas-as-we-know-it/ ↩

8. Thomas Dohmke and Irini Kalliavakou, “Developers, Reinvented”, 2025-08-03. https://ashtom.github.io/developers-reinvented ↩ ↩²

9. Fortune, Sam Altman comments on one-person unicorns, 2024-02-04. https://fortune.com/2024/02/04/sam-altman-one-person-unicorn-silicon-valley-founder-myth/ ↩

10. Bill Gates, GatesNotes, 2026. https://www.gatesnotes.com/meet-bill/tech-thinking/reader/ai-agents ↩ ↩²

11. Peter Diamandis, 2024-02-08. https://www.diamandis.com/blog/abundance-37-no-human-coders ↩

12. OpenAI, “Codex is now generally available”, 2025-10-06. https://openai.com/index/codex-now-generally-available/ ↩

13. OpenAI, “Introducing GPT-5.2-Codex”, 2025-12. https://openai.com/index/introducing-gpt-5-2-codex/ ↩ ↩²

14. Anthropic, “Effective harnesses for long-running agents”, 2025-11. https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents ↩ ↩²

15. Ziqi Yin et al., “Should We Respect LLMs?”, arXiv, 2024-02-22. https://arxiv.org/abs/2402.14531 ↩

16. Om Dobariya and Akhil Kumar, “Mind Your Tone”, arXiv, 2025-10-06. https://arxiv.org/abs/2510.04950 ↩

17. Cheng Li et al., “Large Language Models Understand and Can be Enhanced by Emotional Stimuli”, arXiv, 2023-07-14. https://arxiv.org/abs/2307.11760 ↩

18. Reddit r/LocalLLaMA, “How do you manage quality when AI agents write code faster than humans can review it?”, 2026. https://www.reddit.com/r/LocalLLaMA/comments/1q7hywi/how_do_you_manage_quality_when_ai_agents_write/ ↩

19. LangChain, “The Anatomy of an Agent Harness”, 2026. https://www.langchain.com/blog/the-anatomy-of-an-agent-harness ↩

20. Martin Fowler, “Harness engineering for coding agent users”, 2026-04-02. https://martinfowler.com/articles/harness-engineering.html ↩

21. Ryan Lopfolo, “Harness engineering: leveraging Codex in an agent-first world”, OpenAI, 2026-02-11. https://openai.com/index/harness-engineering/ ↩

22. Varonis Threat Labs, “A Look Inside Claude’s Leaked AI Coding Agent”, 2026-04-03. https://www.varonis.com/blog/claude-code-leak ↩

23. Engineer’s Codex, “Diving into Claude Code’s Source Code Leak”, 2026-04-01. https://read.engineerscodex.com/p/diving-into-claude-codes-source-code ↩

24. Drew Breunig, “How Claude Code Builds a System Prompt”, 2026-04-04. https://www.dbreunig.com/2026/04/04/how-claude-code-builds-a-system-prompt.html ↩

25. Anthropic, “Project Glasswing: Securing critical software for the AI era”, 2026-04-07. https://www.anthropic.com/glasswing ↩ ↩²

26. Axios, “Anthropic: Mythos AI rapidly exploits new software flaws”, 2026-06-08. https://www.axios.com/2026/06/08/exclusive-anthropics-mythos-can-exploit-new-flaws-in-hours ↩

27. Cloudflare, “Project Glasswing: what Mythos showed us”, 2026. https://blog.cloudflare.com/cyber-frontier-models/ ↩

28. Anthropic, “Expanding Project Glasswing”, 2026-06-02. https://www.anthropic.com/news/expanding-project-glasswing ↩

29. BankInfoSecurity, “Claude Mythos Preview Creates Early Edge for Cyber Titans”, 2026-04-09. https://www.bankinfosecurity.com/claude-mythos-preview-creates-early-edge-for-cyber-titans-a-31381 ↩

30. Google AI for Developers, “Build apps in Google AI Studio”, 2026. https://ai.google.dev/gemini-api/docs/aistudio-build-mode ↩

31. Google, “Gemini CLI: your open-source AI agent”, 2025-06-25. https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemini-cli-open-source-ai-agent/ ↩

32. Google for Developers, “Code with Gemini Code Assist”, 2026. https://developers.google.com/gemini-code-assist/docs/write-code-gemini ↩