Something Flipped in December

In July 2025, the most rigorous study ever conducted on AI coding tools found they made experienced developers 19% slower. In February 2026, Stripe is shipping over 1,000 AI-written pull requests per week into a codebase that processes over a trillion dollars annually.

That’s not a typo. That’s six months.

The Study That Aged Like Milk

A randomized controlled trial by METR tracked 16 experienced open-source developers across 246 real-world tasks between February and June 2025. The developers predicted AI would make them 24% faster. After the study, the devs believed it had made them 20% faster. The actual measurement: they were 19% slower.

The study’s lead participant, Domenic Denicola (working on jsdom), documented exactly how: models had “strong, but outdated or wrong, priors,” couldn’t navigate file systems efficiently, and “got stuck in loops” on simple issues. The tools were impressive demos. They weren’t useful for real work.

That study was published in July. By December, the world it described had vanished.

“Coding Agents Basically Didn’t Work Before December”

Something happened in the last few weeks of 2025 that the people who build and use AI tools are still struggling to articulate. Not a gradual improvement. Not the normal march of progress. A flip of a switch.

Andrej Karpathy: founding member of OpenAI, former Director of AI at Tesla, and the individual who coined “vibe coding”, had called coding agent hype “exaggerated” in October 2025. By late January, he was posting this:

LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering. This is easily the biggest change to my basic coding workflow in 2 decades of programming and it happened over the course of a few weeks.

He went from writing 80% of his code by hand in November to 80% agent-generated in December. Two days ago, on February 25, the thread that distilled the moment:

Coding agents basically didn’t work before December and basically work since — the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks.

He described handing an agent a paragraph of plain English to build a local video analysis dashboard: SSH keys, vision model deployment, web UI, system services, all of it. The agent ran for thirty minutes. It hit errors. It researched solutions online. It resolved them one by one. It wrote, tested, and debugged the code. It came back with a finished project and a markdown report. He didn’t touch anything.

Greg Brockman, President of OpenAI, posted something similar this month:

Software development is undergoing a renaissance in front of our eyes. If you haven’t used the tools recently, you likely are underestimating what you’re missing. Since December, there’s been a step function improvement.

Latent Space, one of the most respected AI engineering newsletters, published a piece titled “WTF Happened in December 2025?” The answer, roughly: Claude Opus 4.5, GPT-5, and Gemini 2.0 all shipped within weeks of each other, and each represented a step-function improvement in long-horizon coherence: the ability to hold context, recover from errors, and maintain architectural consistency across thousands of lines of code. The capabilities that make an agent useful for real work, not just impressive demos.

December was the technical inflection. February was when the rest of the world caught up.

“I Haven’t Written Code Since December”

The most startling signal isn’t coming from AI researchers. It’s coming from the people who run companies.

On Spotify’s Q4 earnings call on February 12, co-CEO Gustav Soderström said something that raised some eyebrows:

When I speak to my most senior engineers, the best developers we have, they actually say that they have not written a single line of code since December. They actually only generate code and supervise it.

He described an engineer on their morning commute, from Slack on their phone, telling Claude to fix a bug and add a feature to the iOS app. By the time they arrive at the office, they have a testable build pushed to them for review. This isn’t hypothetical.

Anthropic shipped a feature this week called Remote Control that does exactly this; it connects Claude Code running on your machine to your phone, letting you direct, monitor, and review agent work from anywhere. Spotify’s internal system “Honk,” built on Claude Code, now merges 650+ agent-generated pull requests into production every month.

Shopify CEO Tobi Lutke posted a GitHub contribution graph that had flatlined near zero for the better part of a decade until it spiked vertically in early 2026:

I shipped more code in the last 3 weeks than the decade before. The top AI models / agentic systems right now are an entirely different thing to what people used until the beginning of December.

This week Coinbase CEO Brian Armstrong posted his own contribution graph, a near-decade of near-zero commits ending in a vertical spike.

Armstrong has a master’s in CS from Rice and originally built Coinbase himself. He stopped hands-on coding years ago as the company scaled to a $67 billion market cap. AI brought him back.

Y Combinator CEO Garry Tan posted his commit graph this week too:

About 1/3 of the top technical CEOs are completely AGI pilled by coding again. I am one of them. Highly recommend. Totally exhilarating to be back shipping new products and software again. via X.com

Tan has been staying up until 5 AM writing code with Claude Code; shipping 4,000+ line features in about an hour. He reported that 25% of YC’s latest batch has codebases that are 95% AI-generated, and every one of those founders was “highly technical, completely capable of building their own products from scratch.” They chose not to.

Matt Shumer, CEO of OthersideAI, wrote an essay in early February titled “Something Big Is Happening” that hit 80 million views:

I am no longer needed for the actual technical work of my job. I describe what I want built, in plain English, and it just… appears.

These aren’t researchers hyping their own models. These are people who run companies, ship products, and answer to boards; describing something they observed in their own organizations, independently, all pointing to the same window.

The Numbers Caught Up

METR was, for most of 2025, the best evidence that AI coding was a productivity illusion. Then the tools changed, and METR measured again.

In their update this week , the same class of experienced developers showed a directional shift to an 18% speedup, a swing of 37 percentage points from the original study. METR is careful to note the confidence interval is wide. But they had to redesign the experiment entirely because of a problem that tells the story better than any p-value: developers were refusing to participate in studies that might assign them to the control group. One participant said: “I avoid issues like AI can finish things in 2 hours, but I spend 20 hours.”

When your control group self-selects out because working without AI feels unacceptable, that’s a data point in itself.

METR’s internal telemetry tells a sharper story. Seven staff members using Claude Code across 5,305 sessions in January 2026 measured time savings ranging from 1.5x to 13x on assisted tasks. They call this a “soft upper bound”; but even the conservative end of that range is transformational.

The platform-level data from February paints the same picture at scale.

SemiAnalysis reported on February 5th that 4% of all public GitHub commits are now authored by Claude Code; roughly 135,000 per day. That figure doubled in a single month. Their projection: 20% of daily commits by end of 2026. Dylan Patel wrote: “While you blinked, AI consumed all of software development.”

CircleCI’s 2026 State of Software Delivery report, analyzing 28 million CI workflows, found daily workflow runs increased 59% year-over-year; the largest throughput jump since CircleCI started measuring in 2019. The top 5% of teams nearly doubled their output. The median team improved by only 4%.

OpenAI posted today that Codex weekly users had “more than tripled since the beginning of the year.” The Codex Mac app, launched February 2, hit one million downloads in its first week.

Claude Code’s weekly active users doubled since January 1. Business subscriptions quadrupled. The product hit a $2.5 billion annualized run rate, more than doubling in six weeks.

This isn’t trend data from a 2025 retrospective. This is all February.

The Ground Truth

The macro numbers tell you the direction. The individual stories tell you the magnitude.

Stripe published a deep technical blog post on February 9 about their internal coding agents called “Minions.” These aren’t demos. They’re fully autonomous agents that receive a task via Slack, analyze context, write code, run it against more than 3 million tests, push to CI, and prepare pull requests for human review. Engineers never write code during a Minion run. They only review the output. The result: over 1,000 pull requests merged per week, entirely agent-produced, on a codebase of hundreds of millions of lines of Ruby that processes over a trillion dollars annually.

Jaana Dogan, a principal engineer at Google responsible for the Gemini API, posted in January:

I’m not joking and this isn’t funny. We have been trying to build distributed agent orchestrators at Google since last year. There are various options, not everyone is aligned… I gave Claude Code a description of the problem, it generated what we built last year in an hour.

Michael Truell, CEO of Cursor, described building a new agent harness that orchestrates thousands of coding agents simultaneously, peaking at roughly 1,000 commits per hour over a sustained week.

The Flywheel

The most consequential development isn’t that AI coding tools are getting better. It’s that they’re getting better because AI is building them.

Approximately 90% of Claude Code’s codebase is written by Claude Code, closer to 100% for tests. When Anthropic built Claude Cowork, a non-coding agent product, it was built entirely by Claude Code in roughly 10 days. Traditional development cycles for comparable software run 3–12 months.

OpenAI was explicit about the same loop. When they released GPT-5.3 Codex on February 5, they called it “our first model that was instrumental in creating itself.” Early Codex versions were used to monitor and debug the training runs that produced Codex. The tool was building its own successor.

The results show up in velocity. Claude Code has shipped 337 versions in 367 days, one every 1.1 days. In February, the pace hit 8.4 releases per week, 71% faster than prior months. On February 25, the team shipped 5 releases in a single day, a project record.

The competitive dynamics have compressed from quarterly cadences to something closer to a live arms race. On February 5, Anthropic moved its Claude Opus 4.6 launch forward by 15 minutes. OpenAI responded with GPT-5.3 Codex 16 minutes later. Google shipped Gemini 3.1 Pro within days. Each company is using its own tools to build the next version of its tools. Zvi Mowshowitz, one of the more careful AI analysts, titled his February 12 newsletter: “Welcome to Recursive Self-Improvement.”

Bloomberg captured the mood yesterday: “AI Coding Agents Like Claude Code Are Fueling a Productivity Panic in Tech.”

The Honest Caveats

A responsible reading requires acknowledging what the acceleration costs. There are real problems, and anyone telling you otherwise is selling something.

Quality is measurably worse. CodeRabbit analyzed 470 real-world pull requests and found AI-generated code averaged 1.7x more downstream issues than human-written code. Logic errors were 75% higher. Performance problems were 8x more frequent.

The bottleneck moved, it didn’t disappear. CircleCI found that while feature branch activity surged 59%, build success rates dropped to 70.8%; the lowest in five years. Nearly 3 out of 10 merge attempts are failing. More code is being written. Less of it works on the first try.

Technical debt is accumulating silently. GitClear’s analysis of 211 million changed lines found code duplication increased 48%, duplicate blocks surged 8x, and refactoring collapsed from 25% to under 10% of all changes. The code is being written faster. It’s not being maintained.

Addy Osmani, an engineering leader at Google, calls it the ”80% Problem”: AI rapidly produces 80% of a solution, but the remaining 20%; edge cases, security, production integration, is as hard as it ever was. The danger is mistaking the first 80% for done.

These are genuine risks. They deserve serious attention. And they don’t change what the trend says.

What This Means

Here is the uncomfortable thing about the last three months.

Every metric: task completion time, shipping velocity, throughput, adoption moved in the same direction, at the same time, across every organization measuring it. The study that was the definitive evidence against AI coding had to be redesigned because developers refused to work without the tools. CEOs who hadn’t written code in years are posting their GitHub contribution graphs. Stripe is merging a thousand agent-written PRs a week on production financial infrastructure.

The tools building all of this are themselves being built by AI. The shipping velocity data proves it, each cycle is faster than the last.

Paul Graham posted about a thousand lines of code per hour six months ago and got a 50–50 split between disbelief and recognition. He posted the same stat this week and asked if the ratio had changed. Read the replies. It has.

The code quality data is sobering. The security findings are alarming. The technical debt trajectory is a crisis in the making. The CircleCI data showing that the bottom 75% of teams saw negligible improvement should temper anyone’s expectations of universal uplift.

But the debate over whether AI coding tools work is over. It ended sometime in December, quietly, while most of the industry was on vacation. The aftershock hit in February, and it’s still going.

The new questions are harder: How do you maintain code quality at machine speed? How do you build review and verification systems for a world where writing code is no longer the bottleneck? How do you onboard engineers into a profession that’s being refactored underneath them?

Karpathy framed the real challenge:

The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator agents with all of the right tools, memory and instructions that productively manage multiple parallel instances for you.

The era of typing code into an editor, the way it’s been done since computers were invented, is ending. What replaces it is still being figured out. But the people doing the figuring are building at a pace that, six months ago, would have seemed like a typo in the data.