41 Stats That Tell the Story of AI Right Now

Today on the AI Daily Brief, 41-ish stats that tell the story of AI right now. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick notes before we dive in. First of all, thank you to today's sponsors, Blitzy, Section, Robots and Pencils, and Hyperagent. To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts. And if you want to learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai. Now, one very frequent type of content in the '23, '24, '25 era of the AI Daily Brief was to take some big new report from one of the big professional services firms and analyze all the interesting stats therein about adoption and corporate usage and things like that. I've been doing a lot less of that this year, and it's certainly not because the number of studies or anything have slowed down. I think the reason is that in so many cases, the stats that come out of surveys just feel so disconnected from the reality of AI capability that they feel almost not useful. Now, this is, of course, another byproduct of the shift that happened around the beginning of this year as we got a new set of models. People started to understand the importance of harnesses and agentic AI truly came online in that the world has pretty much separated itself into those who recognize and are trying to adapt to this totally new way of working. And on the other hand, those who are still laboring under some old model. Now, obviously everyone and every company has their own journey in AI. And there are plenty of good reasons why a lot of people who will eventually adopt agents and advanced AI haven't yet. But at the same time, from my standpoint, thinking about the audience that I want to support, I've had less and less energy for trying to convince people to use AI and wanted to instead spend more of that energy on helping people who had already made that decision figure out how to do it well. Thus leading to the AI Summer Adventure and Claw Camp and Agent OS and all those things. And yet, if we are trying to tell a complete story about AI adoption, the truth is that we are still very, very early. And in the many ways, the gap between the people on the frontier and the vanguard and those who are behind is getting wider, not smaller. So with all that in mind, I thought for this particular Long Read Sunday/Weekend Big Think episode, it would be good to go out and check in on all those different surveys that I hadn't spent as much time with to pull out some of the more interesting numbers. So in no particular order, As researched by me with the assistance of both Fable and GPT-5.6 SOL, here are some stats telling the story of AI right now, and frankly the story that we don't always get on the AI Daily Brief. First up, a Gallup poll from mid-May found that 52% of US workers now use AI on the job. It was the first time that we had officially passed the halfway mark, and given that Gallup is going so broadly across all different types of workers, I think that 52% number is carrying a lot of weight. This is basically confirming in my mind that at this point you just use AI on the job, and the questions shift not to how much adoption is there, but how well is it being used. Numbers around ROI are a bit more complicated. A Domino Data Lab study found that while 93% of enterprises reported improved production capability, 57% of those enterprises said that AI's ROI still fails to outpace spend. PwC's mid-year CEO snapshot, which was fielded between mid-May and mid-June, saw 39% of CEOs reporting positive outcomes from AI so far. And I think this is meant to be specifically measurable and tangible outcomes. And this is something you see come up a lot in these surveys. In many cases, we're in this weird hinterland where companies know the value of AI when they see it and know the value of AI on individual and small team levels, but it hasn't translated fully to the organizational level and into measurable bottom line impact. Another version of this story comes from the KPMG Global AI Pulse Survey, this one taken at the beginning of May, where only 7% of global leaders reported established ROI from AI, even though the percentage who said that AI was already delivering meaningful business value had jumped 12 points in a quarter, up to 76%. So again, it's not that AI isn't valuable, it's that the translation all the way to bottom line impact is still very nascent. One story that we talk about a lot here that is now starting to show up in surveys is concerns around cost. Even back at the beginning of May, in an EY AI Pulse survey, 98% of C-suite leaders indicated that token costs were forcing them to reconsider their AI plans. This is, of course, a byproduct of the shift to agentic AI, where you are no longer thinking about AI in terms of number of seats that you are giving people times $20 or $30 a month, and instead thinking about the total cost of intelligence as expressed by used tokens, which can get much, much higher. Now, interestingly, Alongside those 98% who say that token costs have forced them to reconsider their plans, only 64% of them, less than two-thirds, actually meter their usage. For anyone who is trying to figure out how to think about tokens and costs, I'll point you back to last week's Long Read Sunday episode with Nufar, Everything You Need to Know About AI Tokens, which goes deep on all of this. Now, speaking to this idea of a gap between the firms on the vanguard and the firms that are behind, Ramp uses card data from 70,000+ businesses that work with it to provide a portrait of where AI is in businesses that make up their customers. The gap between the average AI usage and the top AI usage is phenomenal. At the median AI-buying business, Ramp found companies spending $11.38 per employee per month. Compare that to the top 1%, which are spending about $7,500 a month. And given that RAMP is kind of an advanced company already, you're talking about businesses that are more likely to be tech-forward than just your average business, showing that this gap is probably even more enormous than this suggests. That RAMP Index also confirmed that Anthropic had flipped OpenAI to be the leader among their businesses in adoption. In their July spend data, 39.5% of their users paid for OpenAI subscriptions, while 42.4% paid for Anthropic. I think it's worth noting that this data also showed that that this is, as everyone has been feeling, really a two-company race at this point. Although how much that changes in the context of this new cost-conscious token efficiency era will be really interesting to see. It's going to be harder to track, but one thing that I'll be keeping an eye on is any sort of numbers that indicate what percentage of companies are experimenting with things like OpenWeights models and fine-tuning strategies or multi-model strategies that take advantage of tools like routers. Let's talk about individual people though. Now, there are some pretty strong dichotomies that show again that the gap between Vanguard and Frontier users as opposed to average users or opposed to resistant users is large and potentially getting larger. One of the more interesting studies from a recent KPMG quarterly pulse survey was that they found that employee resistance to AI agents had quadrupled in a single quarter from 5% to 20%. Now, that's the sort of big jump that feels like it could be noise in the data, but given that KPMG releases their Pulse survey every quarter, that's certainly one to keep an eye on. What's more, AI agents which are more capable and thus perhaps more threatening could certainly plausibly generate more ire among employees who aren't taking full advantage of them yet. And certainly among the employees who are taking advantage, the agentic era has shifted dramatically. According to OpenAI stats that they shared at the end of June, over 25% of Codex users have handed the agent a task that would be estimated to take more than 8 hours of human work. The types of work, in other words, being delegated to AI is increasing significantly. And yet a lot of that activity is still happening in quiet. In a controlled experiment from Atlassian, they found that workers who disclosed their AI use were rated 10 times lazier than identical peers who stayed quiet, which is certainly a big peer pressure reason to not really talk about your AI use, which can create dramatic drag on rates of adoption. In my experience, the most effective way for AI to disseminate across an organization is for power users to take the capabilities, adapt them to the context and needs and unique situation of the firm, and then evangelize within the bounds of those particular usage patterns to the next set of users who then adapt it to their purpose and then evangelize to the next set and so on and so forth. But if those people are all being hammered and side-eyed and seen as not really doing the work because they're using AI, of course they're not going to talk about it. Now, the other reason that some people are keeping their AI usage quiet is that according to PagerDuty, 66% of office professionals have used AI tools that they believed violated company policy. Now, of course, it is my contention that this is not about malicious use. This is about the tools that people have access to outside of work being in many cases dramatically better to the tools that they have inside work. Now, hopefully, as Codex and Cloud Code slash Cowork adoption goes up inside the enterprise, this gap starts to close and there is a little bit less of an incentive for this sort of shadow behavior. But again, that'll be something to watch over the next couple of quarters. We're also starting to see the first numbers in the agentic era of how people are using AI. One stat that I found super interesting came from OpenAI's work at the Frontier Report, which examined more than 800,000 work messages. They found that 43.5% of occupation-specific ChatGPT use at work is for tasks belonging to a different occupation than the user's own. This is, for example, the marketing person making updates to the marketing site instead of waiting for the software engineers to do it. This sort of blurriness is going to have some of the biggest impact on how work changes over the next couple of years, and I think certainly resonates with most people's experience of AI as a capacity-augmenting technology. At the same time, this adaptation is not without bumps. We did an episode a few weeks ago about botsitting, And according to a BCG AI at Work study from June, they found 47% of workers reporting spending more time managing and supervising AI than doing actual work. My contention is that especially in this transition period to agents, this bot-sitting challenge and just agent management type of challenge is going to be particularly acute until we get a little bit more locked in on core patterns and best practices. Still, part of it isn't just based on the transition, but based on the fact that the actual job increasingly will be not to do the work, but to manage the agents that do the work. So ironically, when 47% of workers are reporting spending more time managing and supervising AI than, quote, doing actual work, my contention would be that in the future, for a lot of different roles and positions, managing and supervising AI will be the actual work. That's exactly the world that we're headed into. Every AI coding tool on the market does the same thing first. It starts writing code. Blitzy does the opposite. Before writing a single line, Blitzy spends days reverse engineering your entire codebase. Thousands of agents ingest millions of lines, mapping every dependency, every undocumented constraint, every architectural decision made over the last decade. The result is a dynamic knowledge graph that understands your software the way a principal engineer would after 30 years in the building. Other tools guess at context with grep searches and markdown files. Blitzy never guesses. It builds true understanding first, then delivers over 80% of entire software epics autonomously. Validated, end-to-end tested, production-grade pull requests. That's why Fortune 500 engineering teams trust Blitzy with the codebases that matter most. See for yourself at blitzy.com. That's B-L-I-T-Z-Y dot com. Here's a harsh truth. Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized. Half of companies have AI tools, but only 12% use them for business value. Most employees are still using AI to summarize meeting notes. If you're the one responsible for AI adoption at your company, you need Section. Section is a platform that helps you manage AI transformation across your entire organization. It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value. The result? You go from rolling out tools to driving measurable AI value. Your employees move from meeting summaries to solving actual business problems. And you can prove the ROI. Stop guessing if your AI investment is working. Check out Section at sectionai.com. That's S-E-C-T-I-O-N-A-I.com. I cover the capability gap between AI potential and AI reality every day on this show. Most companies are still figuring out how to start. Robots in Pencils is already launching and scaling. Agentic and generative AI in production at large enterprises in weeks. AWS Advanced Tier Pattern Partner more than doubled in a year. And they're hiring. 50 open roles. If you're someone who knows this moment is different, who wants to be inside it, not watching it, this is worth a look. At Robots and Pencils, the best ideas win, and the team is purposefully kept super high quality. This is the kind of place you look back on as the best decision you ever made. Take a look at robotsandpencils.com/careers. This episode of the AI Daily Brief is brought to you by HyperAgent, where you run fleets of agents your team can manage together. New users get $1,000 in inference. Forget local agents and chat workflows waiting on your laptop to be prompted. HyperAgent deploys always-on agents in the cloud, doing real work across the tools your team already uses. Marketing's agent turns competitor moves into landing pages. Sales' agent enriches leads, drafts emails, and updates the CRM. Ops' agent chases the paperwork and tracks the budget. Every agent has access to shared context and follows your rules about scope and approvals. It's time you add agents that feel like teammates. Hire yours at HyperAgent, built by the team at Airtable. Claim your $1,000 in inference at hyperagent.com/aidailybrief. Now, another number that we used to track closely in the pre-2026 version of the AI Daily Brief was organizations sharing what percentage of their code was written by AI as opposed to written by humans. At this point, given that companies like OpenAI and Anthropic are basically at 100%, these numbers kind of started to get a little less interesting. But given that we are trying to broaden from our understanding just of early adopters to a much wider sector, it is notable that DX found that across more than 500 engineering organizations in Q2 of this year, more than 50% of code was AI generated, and that was up from 34% just a quarter earlier, meaning that the broad shift to software engineering practice is no longer confined to the early adopter organizations, but is pretty much getting everywhere at this point. Now, when it comes to how all these changes to how people work impact how the organization thinks about labor, Again, we are in a very messy and confused period. A ZipRecruiter study from June found that 38% of employers have already shifted basic data entry and processing work away from entry-level workers onto AI. 31% have raised experience requirements for entry-level roles. There has been a lot of hand-wringing around the potential impact of entry-level jobs. Certainly that's something that Anthropic's Dario has loved talking about his concerns around. And yet it's worth noting that 35% of the same sample also expect AI to grow total headcount. Another on the perhaps concerning side, especially for early employees, according to a study of 1,000 hiring managers from Resume Templates, 48% of those hiring managers said that their company would rather invest in AI than hire and train a new graduate. Then again, once again according to Ramp in combination with Revelio Labs, by looking at payroll and spend data on more than 21,000 firms, heavy AI adopters saw an increase of 12% in entry-level hiring growth in the 2 years after adoption. In other words, serious AI spend correlates with more junior-level employees rather than fewer. Is it possible then, not to put it too bluntly, that the companies that are thinking about AI as a way to get rid of junior employees are simply doing it wrong and will eventually figure out what these other companies have and re-increase their hiring in those areas, which they had now been neglecting theoretically because of AI? Related to that, while overall US job postings fell 7%, The Indeed Hiring Lab found 15% growth in software developer postings since February 2025, numbers that certainly don't indicate that all of a sudden developers are being wiped off the face of the planet. Now, for the sake of completeness, it is worth noting that 71% of that growth happened in senior roles. So this still might not be addressing the question of junior-level employees, but certainly we are starting to see more and more evidence that the narrative of AI destroying software development is not only overblown, but outright wrong. One of the big contrasts that we're living with right now is that while AI has been the number one stated reason for US job cuts, according to Challenger, Gray Christmas, for 5 months now, as of August of this year, the Yale Budget Lab has found exactly zero evidence of clear AI fingerprints in aggregate US occupation data, despite us coming up on 3 years since ChatGPT was released. Indeed, I think that there is a growing sense that a lot of the layoffs that are attributed to AI are attributed to AI because it is a politically palatable thing to attribute layoffs to. As I argued recently, I think that that excuse is going to carry less water in the months to come. So we'll have to see if the numbers back me up on that or if AI will continue to be blamed for all manner of different types of layoffs. When it comes to people's concerns about AI, the story is kind of all over the place. On the one hand, an Inside Higher Ed study found that 55% of college students expected AI to hurt their career prospects, with only 7% calling themselves all in on AI. But in a KPMG Summer Intern Pulse study from July of this year, only 5% fear job displacement. And 43% said that their top AI worry was losing critical thinking skills. This is perhaps a gap once again between people imagining what AI is going to be like versus people who are actually using AI and understanding how it intersects with the real world of real work. Also in that KPMG Summer Intern Pulse, basically two-thirds of those surveyed had AI assisting with over a quarter of their assignments. In terms of broad societal issues and opinions, the AI industry continues to face a significant trust deficit. And according to an Anthropic study, admittedly from late last year, although I can't imagine that this has improved very much, only 15% of Americans trust AI companies to decide how AI is developed. Interestingly, a Pew Research Center study from June of this year also found that the perception is that China is ahead of the US in AI. In fact, by 3 to 1 in that Pew study, Americans said that China, not the US, was more advanced in AI. Unsurprisingly, the broader concerns around the AI buildout are also showing up in the numbers. According to a Reuters/Ipsos poll, 77% of Americans— basically an equal share of Republicans and Democrats, by the way— worry that AI will make electricity more expensive, and 57% would oppose a data center in their own community. Now, as I've ranted about on my soapbox many times before, the worry that AI will make electricity more expensive should be a table stakes concern to address for the companies that are building data centers, and hopefully that memo is starting to resonate as this backlash gets louder. Clearly, we're very early and there's a lot of work to be done. In my estimation, one of the biggest gaps in enterprise AI continues to be enablement. Interestingly, in an official European Central Bank survey, the ECB found that just about 50% of firms planned to invest in training their current staff for AI versus only 12% that were planning to hire AI specialists. Now, I don't exactly know how they're going to train those folks given the market's utter failure to provide good solutions for that. If they're anything like their American counterparts, they will build highly bespoke solutions because that's pretty much all that's currently available. And in some of these numbers, you start to see the inklings of more radical changes to come. On AI shopping, Adobe found that on this year's Prime Day, AI-referred shoppers had a 40% better conversion than non-AI-referred shoppers. And certainly it feels like e-commerce is an area that is going to be completely upended by new consumer patterns sooner rather than later. The relationship between work that happens in-house versus from third parties is also likely to undergo some drastic changes. Axiom, for example, in a survey of 528 in-house legal leaders, found that 92% of them either expect or are already negotiating AI-related cuts from outside counsel. Now, that is a phenomenon that is not just going to be unique to legal services, but will be a part of the professional services story as well. I don't think it's as clear-cut as professional services being hacked for internal alternatives, but the way that internal and external firms work with one another is going to shift, I think, dramatically. On the social side of it gets weirder from here, Elon University captured some of the weird zeitgeist of things that are shifting around people's personal experiences with AI. In a study from May, they found that 27% of US adult internet users now had some social or emotional interactions with AI, with 31% of them calling it a friend. Fascinatingly, 74% of those same users predict that AI will deepen society's loneliness, which if you're looking for optimism, is at least a sort of self-awareness that could create some alternative paths forward. And finally, and for the love of God, let's get this one right. According to Common Sense Media, 86% of kids ages 9 to 17 use AI already, and over 40% of them say that no parent has ever discussed AI safety with them. It is my contention that many of the most serious societal issues that we are dealing with right now stem from negative aspects of social media and internet use among young people, particularly around Gen Z. Part of the reason that they got hammered with all these negative consequences of the internet and social media is that their parents, who were mostly full-grown and in jobs before the internet fully came online and certainly before social media came online, just didn't really have a basis of understanding to be able to properly engage with their kids and help them navigate something which would ultimately have huge impacts on social interactions and mental health and so much else. I do not believe that we can afford to make that same mistake with AI, and I think that now is the time for parents and kids to get together and try to learn and understand this together rather than parents just assuming it's something for kids to figure out. This, by the way, is also the type of thing that makes me so frustrated when anti-AI advocates try to pretend like this is a genie that can be put back in the bottle. By not engaging seriously with the reality that AI is here and it is here to stay, they're spending all their energy trying to put toothpaste back in the tube rather than help the people around them both adapt to the new world and have a hand in shaping it. As you can probably see, it really does just feel to me like the gap between different parts of the world when it comes to AI is in a strangely divergent place. And on that front, I will leave you with this thought. You all who are listeners of a show like the AI Daily Brief are, I believe, the most critical actors when it comes to this gap. When push comes to shove, it is not going to be people like me who spend all day every day using AI, talking about AI, creating content around AI, creating content with AI who will shape broad public opinion. It will be the folks who are doing the hard work adopting and adapting to AI, but from within the context of their normal non-AI life, who will be the translators for everyone else. In other words, my conversation is with you all, but your conversations are with everyone else. And as grateful as I am that there are as many of you all as there are, there are still a whole lot more of everyone else. So in other words, you have a huge and important job. No pressure at all. In any case, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching as always, and until next time, peace.