In early 2026, a post by AI luminary Andrej Karpathy (former Director of AI at Tesla, founding member of OpenAI) about his recent intensive programming sessions with Claude quickly went viral in the tech community. With nearly 20 years of experience as a top expert in software development and artificial intelligence, his firsthand experiences and deep reflections reveal the dramatic changes, real challenges, and future possibilities unfolding in the era of AI programming.
This article will provide an in-depth analysis of Karpathy's core insights, offering a glimpse into the true landscape of the AI programming wave.
📱 Karpathy's Original Tweet
This article is based on a tweet published by Andrej Karpathy on January 26, 2026. Original link: View Original Tweet
A Disruptive Workflow Transformation: From 80/20 to 20/80
Karpathy points out that his programming workflow underwent a seismic shift in just a few weeks.
Given the latest lift in LLM coding capability... I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now...
This is a stunning transformation. For a seasoned programmer like Karpathy, this marks a turning point for an entire era. The essence of programming is shifting from writing precise instructions line-by-line to describing intent and logic in natural language, with AI agents handling the concrete implementation. He admits this shift is "a bit of a hit to the ego," but the massive efficiency gains are irresistible. This is perhaps the most profound change to the fundamental software development workflow in the last two decades, comparable to the leap from assembly to high-level languages, or from manual memory management to automatic garbage collection. Developers are no longer bricklayers but architects holding blueprints and directing construction teams.
The Reality of AI Programming: Powerful, But Far From Perfect
While affirming the immense value of AI, Karpathy also soberly points out the many limitations of current AI programming assistants, pouring cold water on hyped-up claims like "no longer needing an IDE" or the omnipotence of "agent swarms."
New Types of Errors: From Syntax to Concept
The mistakes AI makes are no longer simple syntax errors but more subtle, harder-to-detect conceptual errors, akin to those made by a "careless, impatient, but very knowledgeable junior developer."
The most common problems include:
Hallucinated Assumptions: The AI will make certain assumptions for you and proceed without verification. For example, if you ask it to process a file, it might assume the file always exists and is correctly formatted, without writing any error-handling code.
Lack of Reflection: They don't manage their own confusion, seek clarification, expose inconsistencies, weigh trade-offs, or even push back when they should. You might give a flawed instruction that an experienced human programmer would question, but the AI will "faithfully" execute it, leading to a result that deviates from the intent.
Over-complication: AI tends to write bloated, complex code and APIs, abuse abstractions, and fail to clean up the useless code it generates. Karpathy mentions that an AI might use 1000 lines of code to implement an inefficient and fragile feature, but with a single sentence of guidance, it can have a "eureka" moment and optimize it down to 100 lines. It's like asking for a simple sandwich and getting a fully automated sandwich production line in return.
Side Effects: They sometimes modify or delete comments and code that are unrelated to the current task but that the AI dislikes or doesn't understand. This is especially dangerous during refactoring, as the AI might "helpfully" clean up logic it deems redundant but is actually critical.
Karpathy's conclusion is that for any important code, you must "watch them like a hawk" and review and modify it in a powerful IDE. His current workflow is: several Claude conversation windows open on the left, and an IDE on the right for code review and manual edits. This forms an efficient "human-in-the-loop" cycle: the human is responsible for high-quality requirements, high-level design, and final quality assurance; the AI handles tedious implementation, information retrieval, and specific coding tasks.
"Feeling AGI" Moments: Tenacity, Speed, and Leverage
Despite the flaws, Karpathy repeatedly mentions moments of "feeling AGI" (Artificial General Intelligence) while collaborating with AI. These moments are primarily manifested in three aspects:
1. Tenacity
It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going... You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.
Humans get tired and discouraged, but AI does not. This relentless spirit of trial and error dramatically breaks through the bottleneck of human stamina, making it possible to solve complex problems.
2. Speedups & Expansion
Using AI brings more than just "speedups"; it brings "expansion." Karpathy believes he can now do much more than before because:
He can easily implement many features he previously felt were "not worth the time to write." For example, adding detailed command-line argument parsing to a script, generating complete unit tests, or writing beautiful documentation for an internal tool. While valuable, these tasks are often neglected in traditional development due to their low ROI.
He can venture into code areas that were previously inaccessible due to knowledge or skill barriers. A backend engineer can use AI to quickly build a frontend prototype with React or Vue; a data scientist can easily write low-level C++ code that interacts with specific hardware. AI becomes a bridge across the chasm of technology stacks.
AI programming doesn't just make you faster; it enables you to do more, broader, and deeper work. It expands a developer's capability boundary from "I know how to do it" to "I know what I want."
3. Leverage
This is where Karpathy feels the "magic of AGI" the most. The key is to change the mode of interaction.
Don't tell it what to do, give it success criteria and watch it go... Change your approach from imperative to declarative to get the agents looping longer and gain leverage.
Instead of giving step-by-step instructions (imperative), it's better to define the goal and success criteria (declarative) and let the AI loop, test, and iterate on its own until the goal is met. For example, have it write the test cases first, then write the code to pass those tests.
Here's a more concrete example:
Imperative: "Read the 'users.csv' file. Skip the first header row. For each row, split by comma. Take the age from the third column and the city from the fifth. If the age is greater than 30 and the city is 'Beijing', print the name from the first column."
Declarative: "Here is a CSV file 'users.csv' with columns 'Name, ID, Age, Gender, City'. Please find the names of all users who are over 30 years old and live in Beijing."
The second approach gives the AI much more autonomy to choose the optimal implementation path and better reflects its "intelligence." This model maximizes the AI's autonomy, thereby unlocking immense productivity leverage.
The Dual Impact on Developer Experience: Fun and Atrophy
The intervention of AI also brings complex effects to the developer's personal experience.
Increased Fun: Karpathy was surprised to find that programming has become "more fun." Because a large amount of tedious, fill-in-the-blanks work (like writing boilerplate code, debugging low-level errors, looking up API usage) is eliminated, developers can focus more of their energy on the parts that are intellectually stimulating and creative: system architecture design, complex logic-wrangling, and product feature innovation. At the same time, the frustration of getting stuck is reduced. Collaborating with an AI always leads to progress, which encourages developers to take on bolder, more exploratory projects.
Atrophy: He also admits that his ability to write code manually is "slowly atrophying." This stems from the fact that "generating (writing code)" and "discriminating (reading code)" are two different brain functions. Long-term reliance on AI-generated code might make a developer's memory of specific syntax or library functions fuzzy. However, this isn't necessarily a bad thing. Just as we no longer need to memorize complex math formulas because we have calculators, developers might be able to shift their cognitive resources from "how to write" to "what to write" and "whether it's written well." Even if your ability to write code declines, your ability to review code and evaluate design quality can be maintained and may even become more important.
Based on this, he proposes an interesting idea: LLM programming might bifurcate engineers into two groups—one group of "craftsmen" who purely enjoy the art of coding and the joy of building from scratch, and another group of "architects" who are focused on "building products" and using all available tools to achieve business value as efficiently as possible.
Practical Advice: How to Maximize AI Programming Efficiency
Synthesizing Karpathy's experience, we can distill several practical tips for collaborating effectively with AI at this stage:
Think Like a Commander: Your role is to set strategic goals (what to do), not to get bogged down in tactical execution (how to do it). Learn to describe requirements, boundaries, and success criteria in clear, precise natural language.
Become an Excellent Reviewer: Shift your primary focus from "writing" to "reading" and "reviewing." Cultivate critical thinking to quickly identify logical flaws, code smells, and potential risks in AI-generated code.
Leverage Your IDE: Don't abandon your powerful integrated development environment. Use its debugging, refactoring, static analysis, and version control features as a solid foundation for reviewing and integrating AI code.
Iterate in Small Steps, Validate Continuously: Don't expect to give the AI a massive task and get a perfect result in one go. Break down complex problems into smaller chunks, have the AI complete them one by one, and validate and correct at each step.
Learn to Ask and Guide: When the AI gets stuck or produces undesirable output, guide it toward the right direction by asking questions, providing examples, or giving clearer constraints, just as you would with a junior colleague.
Industry Impact: From Individual to Organizational Change
Karpathy's observations signal that the changes are not limited to individual workflows but will profoundly affect the entire software industry.
Impact on Individuals: The center of value is shifting. The importance of pure "coding ability" may decline, while "problem decomposition ability," "system design ability," "product sense," and "cross-domain communication skills" will become more important than ever. Developers need to transform from "code implementers" to "problem solvers."
Impact on Organizations: This poses new challenges for team structures, hiring standards, and technical management. Companies may need to rethink the definitions of junior and senior engineers. Junior engineers might be able to get up to speed quickly with AI's help, but the value of senior engineers—defining architecture, handling complex systems, making critical technical decisions—will become even more pronounced. The culture and process of Code Review will become critically important, serving as the first line of defense against "AI-generated slop."
Future Reflections and Challenges
At the end of his tweet, Karpathy poses several thought-provoking questions and predictions:
Slopacolypse: He predicts that 2026 will be the "slopacolypse," where GitHub, arXiv, social media, and all other digital platforms will be flooded with massive amounts of low-quality, AI-generated content. This will dramatically increase the cost of information filtering, making it harder to find high-quality, original knowledge and code. Reputation and source verification will become new core skills.
What happens to the "10x engineer"? Will the productivity gap between top engineers and average engineers widen with the help of AI? Karpathy believes it is "very likely to dramatically increase." This is because the core strengths of top engineers lie in their superior mental models, architectural abilities, and deep insights into problems—and AI is an amplifier for these abilities. They can better command the AI, issue higher-quality instructions, and thus achieve exponential productivity gains.
Generalists vs. Specialists: Since LLMs are better at filling in details (micro) than devising grand strategies (macro), does this mean generalists will surpass specialists? With AI assistance, a generalist with a broad perspective can easily compensate for knowledge gaps in specific domains, enabling them to integrate different tech stacks and build complete products end-to-end. Meanwhile, specialists whose skills are too narrow and whose work is easily automated may face greater challenges.
The Future of Programming: Will the future of AI programming be like playing a strategy game like StarCraft or Factorio? This analogy is very apt. Developers may no longer be players who manually control every unit, but rather commanders who manage resources (APIs, libraries, compute), plan base construction (system architecture), issue strategic commands (feature requirements), and then watch their AI "units" execute, build, and battle (coding, testing, deploying).
Conclusion: Embracing the "Phase Shift" in Software Engineering
Karpathy's summary is highly insightful:
LLM agent capabilities... have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering... 2026 is going to be a high energy year as the industry metabolizes the new capability.
We are at the tipping point of a "phase shift." The intelligence part of AI has outpaced tool integration, organizational processes, and industry understanding. For all developers, Karpathy's notes are not just a field report from the front lines but a guide to future action. It clearly points out both the challenges and the opportunities.
Instead of worrying about being replaced, it's better to proactively embrace the change: learn how to "program in English," cultivate the mental shift from "imperative" to "declarative" thinking, and hone your critical thinking and architectural skills as a "commander-in-chief." Treat AI as a junior partner with infinite stamina and vast knowledge, who just happens to be a bit foolish at times. 2026, and every year after, will belong to those who best understand how to wield this powerful new lever.
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