The AI Daily Brief: Artificial Intelligence News and Analysis - 51 Charts That Will Shape AI in 2026
Episode Date: December 24, 2025A fast-paced walkthrough of 51 charts that capture where artificial intelligence stands right now and what matters most heading into 2026, spanning capabilities, infrastructure, markets, economics, vi...be coding, jobs, and politics. The episode connects model performance, cost curves, hyperscaler spending, enterprise adoption, ROI data, coding agents, labor impacts, and emerging political pressures into a single narrative about how AI is evolving and what that evolution means for the year ahead Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. https://www.kpmg.us/AIpodcastsBlitzy.com - Go to https://blitzy.com/ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results https://robotsandpencils.com/The Agent Readiness Audit from Superintelligent - Go to https://besuper.ai/ to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai
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Today we are looking at 51 charts that 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.
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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, ChatGyPT, 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 Jen 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.
Open AI 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
line in red, and you can see how at 50 percent it hues really closely to the four-month line,
and at 80 percent, it's mostly on the four-month line with a few recent ones in between the four- and the
seven-month line. Now, whether it's seven months or four months, the point is,
capabilities have not plateaued. They continue to increase dramatically and quickly.
We are also seeing major efficiency gains. This chart shows the performance efficiency of Gemini
3 Flash, which is better performing than Gemini 2.5 Pro, which was state-of-the-art just a few
months ago, for around a third of the cost. Especially as we move into a world where production
workloads are getting bigger and bigger, and we are consuming more tokens, the fact that it's not
just capabilities, but also efficiency and costs that are improving is a big deal.
Another measure of the efficiency gains came with 5.2's performance on the Arc AGI1 exam.
The Arc AGI benchmark folks noted that between a tweaked O3 model last year and GPT 5.2
this year, there was a 390% efficiency gain in a single year.
Now, what this all adds up to in terms of when we get AGI is kind of anyone's guess.
As you can see from this chart, people are all over the place in terms of when they think we're
actually going to get AGI. By the way, there's no common definition of AGI, and there are even
plenty of folks out there who think that the term is getting more and more meaningless.
One interesting note is that I think that, if anything, people's timelines actually got moved
back slightly heading into 2026 from where they were heading into 2025, despite all these
capability gains. Andre Carpathy, in particular, in a big interview he did, might have single-handedly
set back the timeline a couple of years. Now, as we look at these new releases, they are not just
incremental, in many cases they are solving key challenges. The charts we have here are for a long
context test that basically tests how an LLM's performance degrades the more context you give it.
With GBT51, which is like a month old at this point, you can see the performance on this test
went from around 85 or 90% at 8K tokens to a little under 50% at 256K tokens, whereas with GBT52
thinking, it was at 100% to start and stayed very close all the way to the end. This makes that
context window actually usable in a way that it just wasn't before, which is extremely powerful
and opens up new use cases. Still, AI capabilities, as much as they are evolving, are not evolving
evenly. There are a bunch of different versions of this chart that have different size of spikes
depending on how good you think AI is, but the idea here is that AI progress is not uniform.
It is instead jagged, where a model can be superhuman at certain tasks and unbelievably incompetent
at basic things that a kid could do. This jaggedness is a key facet of AI and is part of the challenge
in implementing it well. Indeed, when it comes to what slows down AI, there are a set of three
different bottlenecks. This chart comes from a recent essay by Professor Ethan Malick, who organized it
into capability bottlenecks, process bottlenecks, and verification bottlenecks. Capability bottlenecks are the ones
we think about most, the weaknesses in AI in that jagged performance. Process bottlenecks are the ones that
we really started to reconcile with this year, the things that make it hard to overlay AI onto existing
systems, particularly in the enterprise, and have it do what it's able to do.
A third layer, which we talk about not very much, is a new category that is native and endemic
to AI, which is verification bottlenecks, basically where humans become crucial to reviewing
edge cases and ensuring final accuracy, which is often a whole new set of processes that humans
need to be organized around.
I feel like in some ways, the first profession to really reconcile with these verification bottlenecks
is software engineering, which has seen such a shift over the course of this year in how AI and
agentic coding supports what they do, but has created all these new challenges and shifted a lot of
the work to that verification step. Regardless of the challenges, one thing that this year showed is a
massive explosion of diversity in the model set. The major labs are putting out more different types
of models that have different strengths and weaknesses and are optimized for different types of use
cases, but Chinese labs have also exploded and become a major player when it comes to the choices
that builders have access to. Next up, we move to infrastructure. Obviously, the big theme of this year,
which will continue to dominate heading into next year,
is the hyperscalers making just historically large capital investments
into AI infrastructure in the form of data centers.
This represents one of the largest coordinated technology investments in history,
and something that the market has really had to reconcile and wrap its head around.
The level of investment is why people are asking questions
about whether the output of AI and the revenue that comes from that can possibly justify it.
And yet, all of the big labs feel exactly the same,
which is, as Mark Zuckerberg has articulated many times this year,
it is a much greater risk to underinvest than to overinvest.
Another expression that we got that tells the story of the moment in such a simple chart
is the capital going into office construction versus data center construction.
This was actually from a couple months ago, and I'm sure that it is actually fully flipped
at this point. But starting in 2023, you see a shift where less capital is going into offices
and more capital is going into data centers. And sometime in the middle to the late part of
2025, those lines actually overlapped, where we're now seeing more money spent on data center construction
than on office construction.
Now, one of the things that some people ask
is how much all of this new infrastructure really matters.
This chart shows that slower growth in compute
could lead to substantial delays
of possibly years in terms of certain capability milestones.
Now, it's a whole separate question
as to whether there are actually benefits to those delays.
For example, if you ask Bernie Sanders, there absolutely are.
But the point that this chart is trying to make
is that there will be consequences to the speed of AI development
if these labs don't have access to the compute that they're looking for.
And this chart shows that as much that the labs have to service their existing customers,
they are still heavily investing in the future.
Now, this is 2024, and I'd be interested to see an update for 2025,
but in 24 you can see OpenAI's R&D compute was $5 billion,
as opposed to its inference compute, which was at about $2 billion.
I'm not sure that they were able to maintain this ratio this year.
Given the release of their images model, which was their most viral moment of the year,
plus the release of SORA, plus just continued growth in their base usage,
you have to think that servicing their existing customers started to compete with R&D a little bit more this year
in ways that could be challenging heading into the future. Certainly, there is Scuttlebuck going around that some folks inside OpenAI aren't particularly happy about how that ratio looks right now.
From there, we move into markets. The first chart to note is sort of the most obvious that chatbot adoption is absolutely massive and faster than anything else we've ever seen before. I hardly need to spend a lot of time on this, but we now have two chatbots in ChatGBTBT and Gemini that are absolutely.
absolutely careening towards a billion active users, something that it took the previous
fastest growing technologies five plus years at the very minimum to achieve.
Still, if there was one chart that defined AI for markets this year, it's all of the various
permutations of this circularity chart.
This shows how much revenue and dealmaking flows between the major players like Microsoft
OpenAI and Oracle.
Now, to some, this chart is Exhibit A in why AI is a house of cards.
But of course, what this chart is missing is a visualization of the very significant.
significant and real revenue that is also coming in to help fuel this. Of course, the revenue isn't
even close to the total scale of dealmaking right now, but it is growing faster than anything we've
ever seen, and we really have barely started to scratch the surface on monetization. Still, this chart
is as good at Rorschach test as we have for how you feel about the markets heading into the next year.
Paired with this, we have a recent chart of OpenAI's estimated balance sheet that shows just how much
external capital they're going to need to get to the point where they're actually profitable.
Now, so far it doesn't seem like they're going to have any problem accessing that capital.
The most recent rumors are that they are raising tens of billions of dollars, if not up to $100 billion
at an $830 billion valuation, suggesting that capital markets are very comfortable with what
they're seeing from OpenAI and very willing to fund this party to keep going.
Now, for those who are AI bears, one of the things that they are concerned about is the
reduction in inference costs, whereas models get good enough, AI products can actually run
on much cheaper, simpler GPUs and computers. They worry that if that happens, all of this massive
investment in complex architectures doesn't really make sense anymore. One investor I saw called this chart
the most important and misunderstood chart in AI. Now, it should be noted that not everyone agrees with this
and there are lots of counter arguments, but if we're trying to understand where the market's head is at
heading into next year, this is a key consideration. Now we move into some model competition.
Anthropic, by any measure, had a very good year. Their market share in coding was massive,
and that dragged their market share across the enterprise up as well. According to Menlo Ventures,
estimates, they now claim 40% of the enterprise market ahead of Open AI. Google also saw a lot of
growth in enterprise this year as well. And as open AI's revenue has been growing, Anthropics has been
growing even faster. They went from a billion dollars annualized at the beginning of this year,
and will end the year at somewhere around $8 or $9 billion, it seems. OpenAI started around $4 billion,
and will end the year at $13 or $14 billion, which is absolutely incredible, but still growing more
slowly than Anthropic, at least for the moment. Still, if you were just taking a step back and
don't have a particular horse in this race, the thing to note is just the incredible pace of revenue
growth for both of these companies, which has to be bullish for their ability to actually
make good on all these big deals that they're signing over the course of the next five years.
Another key story of 2025 that sets up the battle for 2026 is, of course, the massive resurgence of Google.
You can see this moment around the launch of GPT5, which also happened to be the launch of the first
version of nanobanana that Gemini really starts to take off. The release of Gemini 3 has also
purportedly even increased this competition, and Gemini is absolutely surging heading into next year.
Another way that you can see this expressed is in the betting markets, where the likelihood
that Alphabet is the largest company by the end of next June has increased significantly.
Nvidia held a commanding percentage of that at the beginning of the year, and Alphabet is now
creeping up on them. Now, the other way to see the sentiment shift between OpenAI and Alphabet is in the
basket of correlated stocks. Bloomberg and Morgan Stanley put together a basket of stocks that are
exposed to Alphabet and a basket of stocks that are exposed to OpenAI and showed how around November
they started diverging, with the Alphabet exposed stocks continuing to rise and the OpenAI exposed stocks
taking a bit of a hit. Now, this doesn't mean anything fundamental. It just means a shift in what
markets believe, but it's a pretty clear and dramatic signal of where things are. Still, if you are
one who is just looking at the performance of these different models, I think one of the most powerful
charts is this one which shows that no one stays on top for long. OpenAI introduces the
world's most powerful model followed by Anthropic, who introduces the world's most powerful
model, followed by Gemini, who introduces the world's most powerful model, followed by Grock,
who introduces the world's most powerful model, on and on forever infinity. Just from a performance
capability standpoint, this is absolutely the chart that best shows what we are going to experience
throughout 2026, I strongly believe. However, if we're talking about the competition between the labs,
we do have to give China its due.
This chart shows the massive increase in China as a share of open source tokens.
At the beginning of the year, it was all meta and mistral, with almost nothing coming from China.
By the end of the year, it was something like 80% Chinese models.
They are a factor heading into next year and will be a bigger factor throughout 2026.
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Next up, we move over to economics, by which we mean the impact of AI.
We've talked already a little bit about how the cost of using AI has fallen precipitously.
What's interesting, of course, is that J-Von's paradox has fully locked in,
and the total amount that enterprises are spending on AI has gone nothing but up.
In fact, price decreases are fueling usage growth as price decreases unlock new types of use
cases that were uneconomical before.
This has led to Enterprise AI being the fastest-scaling software,
category in history. According to Menlo's estimates, it now captures 6% of the $300 billion
global SaaS market. Now, it should be noted that I don't think that the total addressable
market is the $300 billion global SaaS market. I think it is multiples larger than that. But still,
even in historically slow-moving and lumbering enterprise adoption, things are moving very, very
quickly. What's more, despite some rumors to the contrary, companies are actually seeing measurable
ROI from AI even now. In a Wharton study of something like 800 executives, around 70,
75% reported positive ROI from their AI investments. On our AI-R-I benchmarking study,
we found 82% saw current positive ROI. Of the remainder, by the way, only 5.5% were currently
at negative ROI, and even those that were at negative ROI anticipated that becoming ROI
positive by next year. In fact, 96% of overall respondents anticipated positive ROI within the next
12 months. And across the entire sample, 37%, almost 4 in 10 were already seeing high ROI
from their use cases, reporting either significant or transformational impact. In another chart from our
ROI study, we also found a correlation between how diverse an organization's use of AI was and how
much benefit they got. We organized impact into eight different benefit categories, and we found that
when an organization had use cases with just one benefit type, their ROI was lower than
organizations that had four different benefit types, who were lower than organizations.
who had all eight different benefit types in a pretty significant way. Three was a measure of
modest ROI, four was a measure of significant ROI. Organizations that had one benefit type were just over
the edge of modest at 3.13, and organizations that had eight benefit types were starting to creep on
significant at 3.65. What about the idea of 2025 as the year of agents? Well, it turns out in practice,
agents remain nascent. In that same Menlo study, they found about 10 times as much money being spent on
assistance and co-pilots than were being spent on agents so far. In our ROI study, we found something
similar. We divided use cases into three categories, assisted, automated, and agentic, and found 57% were
in the assisted category, 30% were in the automation category, where the AI was managing a discrete
workflow, and 14% were in that agentic category of autonomous work execution. One more in the
economics section that is under-discussed, but I think is going to be extremely important next year.
I anticipate that we're going to start seeing a much deeper integration of ads into the AI landscape.
There are a variety of reasons for that, but they are not just about the business model needs of the labs,
although that's a part of it. LLMs also appear to be a really good platform for ads.
Check out these recent numbers from Similar Web. They looked at the average minutes spent on site after referral,
the average page views on site after referral, and the average conversion rate of referrals,
if the source was ChatGBTBTVT versus Google. And in each case, ChatTPT absolutely thwumped
Google. The average minutes spent on site were three times higher from referrals from chat
GPT. Average page views were 25% higher, and the conversion rate jumped from 5% to 7%. Basically,
people who are finding sites through LLMs are more high intent it appears than your average
Google browser, which again makes it a great place for sponsored links and ads.
Next up, let's look at a category that was extremely important to 2025 vibe coding. Our first chart
is just vibe coding grew really fast. We saw multiple companies surge into the 9th.
figures of revenue, some like cursor creep up on a billion dollars in ARR, and some like Claude
blow past that. The combination of meaningful token cost and high consumption, plus implications
for other use cases in LLMs, made coding-related performance become the industry's number one
priority. As we head into next year, however, especially engineering organizations, are trying to
figure out how to redesign themselves around AI coding. A chart that I've seen from SWIC, Sean Wang,
and others is this semi-async valley of death chart. It looks at Agent Autonomic.
and measures the experience or observed productivity at various autonomy levels.
On the one end of the spectrum, when coding agents are extremely responsive in very fast order,
they can be extremely valuable for deep work focus on the hardest problems.
On the other end of the spectrum, when they have a lot of autonomy,
they can be great for simpler texts that are handled in the background.
The challenge is in the middle range, whereas the chart puts it,
it's not enough to delegate and it's not fun to wait.
Now, different organizations are handling this differently,
and I think Swix even has some questions around whether this is exactly the right way to think about things,
But for our purposes, this chart represents not just the semi-async valley of death,
but just the broader set of questions that engineering organizations are going through
heading into next year to redesign themselves around AI coding.
The reason that this matters outside of software engineering is that I believe that they
are the first department that will fully reorganize themselves around AI capabilities,
and in so doing, set a template that other departments and functions can start to follow.
Another interesting chart showing the impact of vibe coding,
after a long period of being flat, in 2024 and 2025, we started to see the number of apps
and games released to the App Store going back up. The jump in 2025 was particularly acute
going up 25% in a year. Some are attributing this to the rise of vibe coding, and I think that that's
right. Now, for our last two sections, we move into the society-level issues, and the charts that
will shape some of the big debates that we're about to have. This chart has been absolutely everywhere.
The idea of a K-shaped economy, where stocks and asset owners are doing great and everyone else
is doing not so great, has become fairly standard belief at this point, and there are many
who want to attribute it to the launch of ChatsyBT.
There are a ton of other factors like the rate hiking cycle and the return to the mean
after post-COVID over-hiring, but when it comes to politics and society-level conversations,
narratives can often matter more than nuance.
And there are some parts of the economic challenge for people, whether attributable to AI or not,
that are undeniable.
For example, we have the highest youth unemployment rate we've had since about 2015 if you don't take into account the COVID spike.
What's more, to the extent that we are seeing patterns that are maybe actually attributable to AI,
it does look like early career folks are being hit the hardest.
This is a chart of the headcount over time, organized by different career sectors,
and you could see towards the end of 2022 there's a divergence between the mid and senior career folks,
with early career really falling off.
To the extent that AI is taking on all of the junior tasks, there is going to be a really
interesting challenge for us around how people bridge from their early career to their mid-career.
Now, some folks are starting to think about where the job disruption is likely to come.
There were about a million studies this year that were less studies and more predictions
of which types of jobs are going to be most subject to disruption.
One really valuable chart came out of Stanford, who divided tasks and roles based on where
workers desired automation and based on where AI was actually capable.
of automation. Rolls in tasks, where workers desired automation and where AI was capable, is what they
called the green light zone. Tasks where automation desire was high, but automation capability was
low, they called the R&D Opportunity Zone. Tasks where capability was high, but desire was low,
is what they called the red light zone. And unfortunately, some others looked there and found that a lot
of, for example, why Combinator startups were working in that red light zone, which I think more
than anything reflects just the fact that we need to be having these conversations more around where we
actually want automation. Now, as narratives take hold of AI labor disruption, some studies are also
pointing out that counterfactually, that's not necessarily the only thing that's showing up.
A recent study, for example, showed that in terms of both wage growth and overall job growth,
occupations with high AI exposure, at least right now, are growing much more significantly than
those with low exposure. All of which sets us up for the politics conversation.
Miriam Webster's word of the year was slop, and I had tweeted that I think that it tells you
all you need to know about the difference of our perspective inside the AI industry than outside,
that it was slop and not something like vibe coding that was the word of the year.
And yet, as much as it seems like AI is going to become an issue, at least for right now,
most folks don't rate it super highly as something they care about.
Only 7% of people polled had AI in their top five most important issues.
That said, they definitely don't want companies to have.
a free hand. Recent polling around the White House executive order to ban state-level regulation
had pretty strong opposition, 55% opposed to just 18% supporting it with 27% not sure. And while
broadly, the issue may not be clear, data center politics are starting to emerge as a local
issue. It's still very nascent, but in a couple of elections we saw this year, it was a meaningful
part of the discourse. I would expect to see a lot more of that heading into the midterms next year.
So there you have it, my friends, 51 charts that explain AI heading into 2026.
Hopefully this was an interesting lens to look at things through.
I will have a link to this presentation if you want to download it on AIDailybrief.aI.
For now, that is going to do it for today's episode.
Appreciate you watching or listening.
And until next time, peace.