Published on December 27, 2025 8:16 AM GMT

Andrej Karpathy posted 12 hours ago (emphasis mine):

I’ve never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue. There’s a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering. Clearly some powerful alien tool was handed around except it comes with no manual and everyone has to figure out how to hold it and operate it, while the resulting magnitude 9 earthquake is rocking the profession. Roll up your sleeves to not fall behind.

This seems to be a big update since his Dwarkesh episode published on Oct 17 (though I know these things can take a while to get edited, so the gap could be even bigger), where he said:

Overall, the models are not there. I feel like the industry is making too big of a jump and is trying to pretend like this is amazing, and it’s not. It’s slop. They’re not coming to terms with it, and maybe they’re trying to fundraise or something like that. I’m not sure what’s going on, but we’re at this intermediate stage. The models are amazing. They still need a lot of work. For now, autocomplete is my sweet spot. But sometimes, for some types of code, I will go to an LLM agent.

This is just me guessing, but Claude Opus 4.5 released just one month ago, and Opus 4.5 + Claude Code seems like the big shift for a lot of people.

In fact, Boris Cherny, creator of Claude Code, commented on Karpathy’s post saying (emphasis mine):

I feel this way most weeks tbh. Sometimes I start approaching a problem manually, and have to remind myself “claude can probably do this”. Recently we were debugging a memory leak in Claude Code, and I started approaching it the old fashioned way: connecting a profiler, using the app, pausing the profiler, manually looking through heap allocations. My coworker was looking at the same issue, and just asked Claude to make a heap dump, then read the dump to look for retained objects that probably shouldn’t be there; Claude 1-shotted it and put up a PR. The same thing happens most weeks. In a way, newer coworkers and even new grads that don’t make all sorts of assumptions about what the model can and can’t do — legacy memories formed when using old models — are able to use the model most effectively. It takes significant mental work to re-adjust to what the model can do every month or two, as models continue to become better and better at coding and engineering. The last month was my first month as an engineer that I didn’t open an IDE at all. Opus 4.5 wrote around 200 PRs, every single line. Software engineering is radically changing, and the hardest part even for early adopters and practitioners like us is to continue to re-adjust our expectations. And this is still just the beginning.

To be clear, a lot of these PRs might be “quite small, a few lines and bug fixes” (cf. this comment by another Anthropic employee). Boris had just asked users for feedback, then closed 19 PRs the next morning. Still, 200 PRs in a month without opening an IDE is something ^[1].

AI Accelerating AI

It seems like we might be entering something like a self-improving feedback loop for the system “humans + AI”: employees at the labs are developing AI coding agents using these same AI coding agents, with the horizon length of these models increasing on a faster exponential than we thought (cf. Opus 4.5), and potentially not even an exponential.

This isn’t AI autonomously improving itself, but the feedback loop between training better AI models and having these models accelerate the automation of AI R&D seems to be tightening ^[2].

The “Coding Overhang”

In July 2020, after GPT-3, Andy Jones asked if we were in an AI Overhang, because (at the time) it felt like companies could just be scaling models like GPT-3 to many more orders of magnitude and get much more “intelligence”.

With coding agents and reasoning / test-time compute, it seems to me that what Karpathy (& Boris) are describing is some sort of “Coding Overhang” where people at the cutting edge, and especially members of technical staff, are trying to catch up with ~10x improvements that are purely user-dependent skill-issues.

“I’ve actually been enjoying the last days of software development. There’s a touch of sadness, but there’s also something about knowing we’re near the end that makes it novel and sweet again.” - Moxie Marlinspike (creator of Signal)

In what worlds do we not get Superhuman Coders by the end of 2026?

1. Note: As the creator of Claude Code, Boris is obviously incentivized to promote it.

2. See this older 2021 post I wrote about self-improving {humans + AI} systems, or this video explaining Tom Davidson’s full takeoff model for more intuitions.