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Today's episode is brought to you by my company, Superintelligent. Superintelligent is an AI planning platform, and right now, as we head into 2026, the big theme that we're seeing among the enterprises that we work with is a real determination to make 2026 a year of scaled AI deployments, not just more pilots and experiments. However, many of our partners are stuck on some AI plateau. It might be issues of governance, it might be issues of data readiness. It might be issues of process mapping. Whatever the case, we're launching a new type of assessment called Plateau Breaker that, as you probably guessed from that name, is about breaking through AI plateaus. We'll deploy voice agents to collect information and diagnose what the real bottlenecks are that are keeping you on that plateau. From there, we put together a blueprint and an action plan that helps you move right through that plateau into full scale deployment and real roi. If you're interested in learning more about Plateau Breaker, shoot us a note. ContacteeSuper AI with Plateau in the subject line, Meet Rovo, your AI powered teammate. Rovo unleashes the potential of your team with AI powered Search chat and agents or build your own agent with Studio. Rovo is powered by your organization's knowledge and lives on Atlassian's trusted and secure platform, so it's always working in the context of your work. Connect Rovo to your favorite SaaS app so no knowledge gets left behind. Rovo runs on the Teamwork Graph, Atlassian's intelligence layer that unifies data across all of your apps and delivers personalized AI insights from day one. Robo is already built into Jira Confluence and Jira Service Management Standard Premium and Enterprise subscriptions. Know the feeling when AI turns from tool to teammate? If you Rovo, you know. Discover Rovo, your new AI teammate powered by Atlassian get started at ROV as in victory o.com.

B (10:19)

AI isn't a one off project. It's a partnership that has to evolve as the technology does. Robots and pencils work side by side with clients to bring practical AI into every phase automation, personalization, decision support and optimization. They prove what works through applied experimentation and build systems that amplify human potential. As an AWS Certified Partner with Global Delivery Centers, Robots and Pencils combines reach with high touch service where others hand off. They stay engaged because partnership isn't a project plan, it's a commitment. As AI advances, so will their solutions. That's long term value. Progress starts with the right partner. Start with Robots and pencils@ropotsandpencils.com aidaily Brief this episode is brought to you by Blitzy, the enterprise autonomous software development platform with infinite code context. Blitzi uses thousands of specialized AI agents that think for hours to understand enterprise scale code bases with millions of lines of code. Enterprise engineering leaders start every development sprint with the Blitzi platform, bringing in their development requirements. The Blitzi platform provides a plan, then generates and pre compiles code for each task. Blitzi delivers 80% plus of the development work autonomously while providing a guide for the final 20% of human development work required to complete the Sprint. Public Companies are achieving a 5x engineering velocity increase when incorporating Blitzi as their pre IDE development tool, pairing it with their coding pilot of choice. To bring an AI native SDLC into their org, visit blitzi.com and press get a demo to learn how Blitzi transforms your SDLC from AI Assisted to AI native.

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Welcome back to the AI Daily Brief. Over the past few days we have gotten a slew of reports that deal in some way with the state of AI in the business world. Now, interestingly, over the course of 2026, especially the back half, this conversation has gotten a lot more significant. Whereas before it mattered mostly to the people who were in enterprises and trying to figure out where they stood relative to AI adoption, now there is a whole additional dimension based on the broader questions of boom versus Bubble that dominate market conversation. Basically, as I've said before, there's really no way to prove if it is a bubble in the sense that the things that would show us that, for example OpenAI isn't going to be able to meet its 1.4 trillion in commitments are so far in the future that it leaves investors looking for evidence now of why those things might happen. Because the things that would be the bubble popping can't happen yet. And that's where enterprise adoption becomes really important. It is one of the areas that can both a continue to show growth and boom type behavior and b in how much opportunity remains show that there is still big revenue room to grow. The two reports we're looking at today are OpenAI's State of Enterprise AI and Menlo Ventures third annual State of Generative AI in the Enterprise. Together they tell some pretty common stories. Adoption is up, people are reporting roi, Coding is clearly the first killer app agents require a lot of work still, and the gap between leaders and laggers is growing. So let's look at the OpenAI report. First, from a methodology perspective. This analysis draws from one as you would imagine, real world usage data from OpenAI Enterprise customers, and secondly, a survey that the company did of 9,000 workers across almost 100 companies that documented their patterns of AI adoption. Story one to the shock of no one, at least no one listening to this episode, is that adoption is growing in major ways. ChatGPT Enterprise seats have increased 900% year over year. Since November of last year, weekly enterprise messages have grown approximately 800%. The average worker is now sending 30% more messages than they were a year ago. And importantly, this underscores another part of the story, which is that it's not just that usage is more frequent, it's also deeper. While we will see towards the end of this episode that we still have a long way to go when it comes to making highly functional and truly autonomous agents, there is clearly a shift away from very surface level work towards much deeper work and greater levels of automation and integration into core workflows. Indeed, while enterprise messages have grown 8x in aggregate, the number of weekly users of custom GPTs and projects is up 19x. So why does that matter? Well, custom GPTs and projects are basically alternative interfaces for interacting with ChatGPT that can be configured with different types of instructions, custom actions, specific knowledge bases and context. Which means that especially in an enterprise context, they're going to be frequently used for common, repeatable multi step tasks. So the fact that this is growing more than twice as fast as overall usage suggest that enterprise users are not just, like I said, getting broader, but also going deeper. In recent months they wrote about 20% of all enterprise messages were processed via one of these custom GPTs or projects. When it comes to different industries, the short of it is that they are all up, although some are up more than others. The median sector right now expanded 6x year over year, but even the slowest growing sector, which for OpenAI was education services, was still up 2x year over year. The fastest growing sectors overall are technology, healthcare and manufacturing at 11x, 8x and 7x growth respectively. In another episode recently where we talked about the Open Router study, one of their big reflections was the dramatic shift away from non reasoning tokens towards reasoning tokens. And sure enough, OpenAI saw that as well. And in fact these numbers are maybe even more dramatic. In the past 12 months they found that average reasoning token consumption per organization is is up 320x now to be fair, that was from a starting point of near zero a year ago because reasoning models were at this time just coming online. But still, I think a big part of the story of why we've seen such massive adoption this year is that these reasoning models allow for more complex and sophisticated work. But what about whether people are actually getting value from this? I am of course swimming in ROI data right now. For those of you who contributed to the AI ROI benchmarking study, by the way, appreciate your patience. We ended up with over 5,000 use cases being shared, so we're taking a little bit longer to do even more analysis than we had anticipated and that'll be coming next week. In any case. As a little bit of a preview, I will say that what we found is a significant amount of self reported ROI and that is exactly what you get from OpenAI study as well. They wrote that 75% of those surveyed workers reported that using AI has improved either the speed or quality of their output. Users of ChatGPT Enterprise are saving between 40 and 60 minutes a day, with certain categories like data, science, engineering and communication saving more like 60 to 80 minutes per day, 87% of IT workers report faster IT resolution, 85% of marketing and product users report faster campaign execution, 75% of HR professionals report improved employee engagement, and 73% of engineers report faster code delivery Which I think is important not just for the magnitude of the numbers, but but for the breadth of impact across the organization. Now, if you are a regular listener, you will know that one of the things that really interests me is when AI moves from an efficiency technology, time savings, cost savings, etc. To an opportunity technology where people are doing new things that weren't possible before. And that shows up in a major way. In this OpenAI study, 75% of workers they surveyed reported being able to complete tasks they previously could not perform. Those tasks include things like spreadsheet analysis and automation, technical tool development, custom GPT or agent design, programming support, code review. And the biggest part of this, which isn't surprising even though it is still profound, is the rise of coding related messages in areas outside of engineering, IT and research. They found that among ChatGPT enterprise users, those messages, which are again outside of traditional technical functions, grew an average of 36% over the past six months. And frankly, I think those numbers are a little soft because as you know, if you are a non technical vibe coder, what percentage of your vibe coding are you doing in the main ChatGPT interface versus in some other tool or IDE or platform? Now, of course in the enterprise there are going to be more restrictions on where you can engage. So it's not a one to one comparison to how you might do things personally. But still, what I'm saying is that I think that that 36% growth in non technical coding related messages definitely represents the bottom end of what that growth actually is. Now, one of the most interesting things that I wanted to harp on here, because it's a theme that I see coming up over and over again, and I'm increasingly convinced is going to shape a lot of the beginning of 2026, is that rather than everyone rising at the same rate, we're seeing compounding growth from the people who are getting out ahead. In other words, the people in organizations who are using AI the most are getting more value from it and translating that value back into faster growth which allows them to get farther ahead relative to their peers. The gap between leaders and laggers is increasing because what constitutes that gap is making the leaders grow faster than the laggers. There's some evidence of this on both the individual and the organization level in this report. Basically, ChatGPT found when it came to individuals, the people who consumed the most intelligence as measured by credits used reported higher time savings. The group that saved over 10 hours a week used eight times more credits than the group who reported saving zero hours a week. Frontier workers, who OpenAI defines as in the 95th percentage of adoption intensity, generate six times as many messages as the median worker. What they use it for is also different. Frontier users send 8 times as many creative media related messages, 9 times as many information gathering messages, 10 times as many analysis and calculation messages, 11 times as many writing and communication messages, and 17 times as many coding related messages. At the firm level, Frontier firms, which again are in the 95th percentile, generate twice as many messages per seat overall and seven times as many messages to those custom GPTs where a lot of contextual knowledge and workflows live. OpenAI writes. These firms invest systematically in the infrastructure and operating models required to embed AI as a core organizational capability rather than a peripheral productivity tool. So that's the story from this OpenAI report. Let's move over into the Menlo report. As I said, the background noise for this one is definitely the boom versus Bubble conversation. And in their minds, everything that they found in this third annual study points strongly to boom territory. The Menlo study comes from a survey of 495 US enterprise AI decision makers, meaning technical leaders, VPs of engineering and product and C suite executives that was conducted in partnership with an independent research firm, with the interviews happening last month between November 7th and 25th. So very recently. Once again, big conclusion number one is that this thing is growing fast. Menlo calls it the fastest scaling software category in history, which at 37 billion in spend this year captures 6% of the 300 billion global SaaS market just three years after ChatGPT was released. Now as one small quibble, which isn't really a quibble with Menlo, but more just something that I think is important for our own understanding. On the one hand, we kind of have to call AI a software category, but I think that that not only really undersells what it is, I also think it's quite distracting for these enterprise users who are trying to figure out how to make sense of this. A software category is something that can fit comfortably on a Gartner Magic Quadrant AI doesn't. It is a total systems change enabled by a broad and diverse category of software that should ultimately have a significantly larger total addressable market than the SaaS market, given that it's going to remake so much of what gets done now, not just replace existing software. The report also once again validated the idea of coding as Gen AI's first killer use case. According to Menlo, enterprises spent 4 billion on AI coding this year, which was 55% of what they call overall departmental AI spend. By way of comparison, marketing AI spend was just 9%. The individual categories of AI coding are all up as well. Code completion is up 5.1x, AI app builders are up 10x and code agents are up 36.7x. In many ways it turns out that 2025 was the year of AI agents. It was just coding agents, not general agents. Now related to coding being the killer app, Menlo argues that at this point Anthropic is fairly definitively the enterprise AI leader. They estimate that anthropic now earns 40% of enterprise LLM spend, which was up from 24% in 2024 and 12% in 2023. Google also saw a big jump, going from 7% in 23 to 21% in 25. And all this happened at the expense a little bit of meta, which went from 16% in 23 to 8% in 25, and a lot at OpenAI, which went from 50% in 23 down to 27% in 25. Now this is well trodden territory. It's why, among other reasons, that OpenAI is as aggressively focused on Codex and their coding models as they are. By the way, Menlo also puts Anthropics 2025 coding market share at 54% compared to OpenAI's 21% and Google's 11%. In terms of what exactly companies were spending on, the big trend that Menlo noticed here was the rise of the application layer, which they clocked at 19 billion compared to infrastructure's 18 billion. Horizontal AI was up 5.3x, departmental AI up 4.1x and vertical AI up 2.9x. And interestingly, alongside the growth in application layer, AI startups also made up a lot of ground versus incumbents. Now, one of the interesting phenomena about AI, as compared to some previous technology waves, is that the incumbents had many more advantages than they are used to when it comes to a new technology. Usually the way these things work is startups come in and innovate, taking advantage of their nimbleness and ability to iterate more quickly. And eventually some gain traction and become the incumbents of the future. However, given how deeply integrated AI is to the enterprise ecosystem, the entrenched distribution, the data moats, the enterprise relationships, the scaled sales teams, all of that puts points in the incumbent's favor. However, between 24 and 25, Menlo saw a flippening. In 2024, 64% of enterprises said they preferred buying from incumbents. But this year, in actual Reality startups captured about $2 in revenue for every $1 that was earned by incumbents in the application layer, with startups commanding 63% of the market overall. Part of that was to do with where companies were focused, the success of cursor versus GitHub, copilot, and in sales, the success of AI native startups like Clay and Actively. Now, on a similar theme of how enterprises are engaging with AI, the full build versus buy boomerang has finally completed. Back in 2023's enterprise survey, Menlo found that 80% of enterprises were buying software versus just 20% building. But that shifted dramatically in 2024 when they found 47% of solutions being developed in house with 53% being sourced from vendors. Now I argued at the time that what that reflected was 1 a growing confidence of enterprises in engaging with AI, but 2 the immaturity of vertical and departmental and functional level startups that I thought was going to change pretty quickly. It just seemed to me that even with increased confidence and even with the decreased cost of building software, once highly focused application layer startups came online for key use cases, I thought that was going to boomerang back to about that 8020 split. Sure enough, in this study they found 76% of AI use cases being purchased rather than built internally. This does not mean that enterprises aren't engaging with and excited to build certain categories of software. In fact, I would argue that the buy build paradigm is the blurriest it's ever been. When it's come to AI, there's really nothing that's truly off the shelf. There is always going to be some amount of integration, wiring up the data, permissioning governance that's going to involve technical work from the enterprise. In fact, that's one of the things that's slowing adoption. But yes, in general I think that this 3/4 or 4/5 split between buying and building is a little bit more what I would expect going forward. Now, two more things that I want to talk about quickly before we get out of here. One is a specific follow up to what we discussed in the Open Router study that found such growing adoption of Chinese models that is nowhere to be seen among the Enterprise open source LLMs in general are down from 19% in 24 to 11% today. Manilow speculates that in fact part of that is the stagnation of Meta's lama models. Enterprises are very clearly wary of Chinese open source models which account for just 10% of enterprise open source usage, which means just 1% of total LLM API usage overall. Now that is of course very different than what we're seeing across developers. Frankly, this is one that I don't expect to change all that much, especially with all of the closed source model providers working on ever more performance small, cheaper models. I just think that the morass of dealing with China originated models isn't going to be worth it for most companies. Lastly, as much excitement as there is about the future of agents right now, that is not where most companies are. Copilots represent 10 times as much enterprise spend as do agents, and part of that is because, as Menlo puts it, a modern AI stack is still in development. They point out that overall, AI architectures remain surprisingly simple even in production. They found that only 16% of enterprise deployments qualify as true agentic systems, and even Those are relatively simple 39% are fixed sequence workflows, as opposed to just 8% that are, for example, multi agents. Now, as I was prepping this, I noticed that Anthropic had dropped a new report about how Enterprises are building AI agents 2026, but I think we're going to have to save that for a deeper dive on the state of agents in the Enterprise. For now, that is the story of enterprise AI, at least through the lens of this OpenAI study and the MENLO study. Over the next couple weeks, I'll be doing a number of episodes trying to turn this into some practical ideas about what the future might look like. For now, that's going to do it for the AI Daily Brief. Appreciate you listening or watching. As always. And until next time, peace.