Today we are looking at 51 charts to tell the story of artificial intelligence heading into next year.

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

All right, friends, quick announcements before we dive in.

First of all, thank you to today's sponsors, KPMG, Super Intelligent, Robots and Pencils and Blitzy. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can subscribe on Apple Podcasts. If you were interested in learning about sponsoring the show, send us a note at sponsors at AIDailydief.a.i. And if you want to learn more about our recently released AI ROI benchmarking survey, you can find out all about that at AIDBintel.com. Now, we are in the midst of end-of-year episodes, which is a combination, of course, of both looking back and looking forward. And this episode is all about the charts that sit right at that intersection.

There are charts that tell us where AI is today and give us some idea of what we should be

planning on heading into 2026. Given that there are 51 of these things, I am going to rip through

them, so buckle up and let's talk about the 51 charts that explain AI in 2026.

Quick note on the production of this, the charts were all sourced entirely by me. Part of my process for preparing this show is spending a ton of time on X slash Twitter and using those bookmarks heavily, and I have a folder where I actually keep these types of charts. So step one was just going back and looking at the charts that I thought were most reflective of the current moment and had something to say about the year we're heading into. The second part of the process was outlining a somewhat rough organization of which charts I wanted to include. From there, I turned it over to Claude, ChatGPT, and Gemini to see how they would organize it. I liked Opus 4.5 best, so we went with that with a few tweaks, and then I handed that and the charts off to Gen Spark and Manus to put it all together. And while Gen Spark looked much better, it made some really weird leaps in terms of how it was describing things and had some errors. So ultimately, we went with Manus, which was then exported to Google Drive for a final edit by me. Apologies for those of you who don't care about that. I just think a lot of you are also interested in the operator and production side of AI, so I like telling you how these things get put together. All right. As you can see, we've divided this into seven categories, capabilities, infrastructure, markets, economics, vibe coding, jobs, and politics. We kick off with capabilities. First chart comes from OpenRouter and is the reasoning versus

non-reasoning token trends over time. You've probably seen this one a couple times now. Basically,

at the beginning of 2025, reasoning models were not yet really a thing. OpenAI had announced

O1 preview back in September, and it had finally become available at the very end of December,

but we were just starting to get our hands on these things. That would change dramatically over the course of the last year, and by

November of 2025, reasoning tokens represented meaningfully over 50%. This has brought with it new capabilities,

new use cases, and new ways of thinking about how we scale. Our next chart is the one that for much

of this year held up the entire world it felt like.

This is the chart from Meter that measures the time horizon of software engineering tasks

that different LLMs can complete at 50 and 80% success rates.

So the task duration here is not how long the model works for independently, it's how long

in human equivalent time a task can complete. Coming into this year,

meter had shown a doubling of capability roughly every seven months, but it had started to inch up

to closer to four months, and this year reified that four-month doubling time. In these charts,

you can see the seven-month doubling line in green and the five-month doubling line in red,

...