The State of AI Q2: AI's Second Moment

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

“Alright, friends, quick announcements before we dive in.”

First of all, thank you to today's sponsors, KPMG, Blitzie, Super Intelligent and Robots and Pencils. To get an ad free version of the show, go to patreon.com/aideally brief or you can subscribe on Apple Podcasts. To learn more about sponsoring the show send us a note at sponsors@aideally brief.ai. Now, this week, the deal is that I am traveling with my family, and so the plan is to not be doing our regularly scheduled episodes. Instead, this is the AI Daily Brief's Q2 Build Week.

We're going to have shows with a much more practical bent, including a show all about our new maturity map benchmark,

“a masterclass on using skills with new fart gas bar, and even the ultimate AI catchup guide to share with friends and family who are just getting started on their AI journey.”

Today, we're going to kick it all off with something that I wanted to start doing for a while, which is a big quarterly state of AI report. You can find the full 87 slides at q2.aidbintel.com, as well as on play.aiideally brief.ai, but we'll be going through all the highlights here. The big theme, of course, is what we've been exploring all quarter, which is the idea of AI second moment, and the implications as the capability gap grows. The sources for this, of course, include all of the episodes from the previous quarter, all of our pulse survey results, plus more than 400 sources that are constantly being explored and debated by our team of open claw researchers.

This was in short the most consequential quarter in AI since chatGBT launched. In fact, this is why I'm calling this AI second moment. If the first moment was viable, AI assistant experiences via chat bots like chatGBT, the second moment is all about workable agent systems. Now, the stakes of the second moment are significantly higher than the stakes we're back in 2022.

The capabilities have scaled up dramatically. We've gone from the fastest growing app in history with 100 million users in its first five weeks to billions of weekly active users across platforms.

The economic stakes have gone from speculative venture bets to a plan $650 billion in capex this year, $400 billion in a SaaS apocalypse wipeout, and single funding rounds worth tenser even $100 billion. The corporate reality has gone from the very first explorations of AI to AI first mandates, 40% staff cuts, and total reorientation of the enterprise, and of course we are finally emerging into a period of greater political volatility around AI as well. Let's talk first about the inflection point. Something clicked over the holidays. The combination of the new set of models including Opus 4.5 and GPT 5.2 plus the hardest capabilities of cloud code and codex were clearly transformative, but it actually took people going away and having some time away from their normal pace of work.

“To see just how much it changed. I remember when I saw mid-Journey CEO David Holds say, "I've done more personal coding projects over the Christmas break than in the previous 10 years combined that we were in for something big this year."”

Now at the core of this is obviously cloud code. While cloud code was first introduced last March, throughout 2025 we came to understand that cloud code was fundamentally misnamed, at least in terms of how many people were using it. Even before this burst of activity, non-technical people were using cloud code for all sorts of non-coding use cases, previewing a lot of the key trends from Q1 of 2026.

Throughout Q1, cloud code grew spectacularly from 1 billion in revenue to 2.5 billion in annualized revenue in just a couple of months.

But last quarter was also the quarter when cloud code style capabilities came for the rest of knowledge work in the form of cloud co-work. It was launched in January and within a couple weeks we had not only a lot of technology users, but even significant market reactions. The information reported that co-work triggered emergency meetings at Microsoft, and when we learned that co-work had been entirely built with cloud code, it put a really fine point on just how much had changed about software engineering.

And even though much of the story of the last quarter was focused on the products through which we used the models like cloud code and cloud co-work, we also got more frontier capabilities shipped in the last 90 days than any quarter in AI history. We got in sequential order, GPT 5.2 codex, Genie 3, the first playable version of their world model, Opus 4.6, GPT 5.3 codex, Sonnet 4.6, Gemini 3.1 Pro, Nano Banana 2 and GPT 5.4. And what was interesting is that at this point, it's very clear that there is no single benchmark winner across all different use cases.

If you look at many of the most common benchmarks, GPQA diamonds, we benchmarked terminal bench, the meter long task horizon GDP valve, there is a constant jogging between the latest Gemini GPT or cloud model, and they all tend to be within a very small reach of one another. When it came to markets, if Q4 2025 was all about the AI bubble narrative, Q1 was the quarter when cloud code killed the AI bubble. We had public recantings of previous AI skepticism from people like Legendary Investor Howard Marks, and in general the story about AI moved from what if it doesn't get any better, and what that means for the infrastructure build out,

to is this going to take all the jobs in very short order. Now with all of that prelude, the output of the inflection point was an explosion in the world of agents.

From humble origins as cloudbot back in January, to a very brief stint as multiple, to finally reaching its final form as Opin Cloud, and eventually being recruited into Open AI just a couple weeks later,

Opin Cloud became the most start open source project on GitHub ever, Nvidia CEO Jensen Huang called it maybe the most important software release ever,

and effectively the rest of the industry was racing to integrate cloud type features as fast as they could. We saw Opin Cloud type capabilities from notion in their custom agents, from proplexity with proplexity computer, Nvidia actually announced a version of Opin Cloud called NemoClaw, that was an enterprise-grade wrapper around it, and anthropic has just been going feature by feature, bringing into the native cloud code and cloud core ecosystem all of the things that people love about Opin Cloud.

In the last 30 days at the time of recording, we've gotten remote control, dispatch, computer use, scheduled tasks, projects in co-work, and a whole bunch more. And while OpenClaw and Cloud might have dominated a lot of the conversation inside the AI industry, by just about any measure, Opin AI had a very, very good quarter as well.

“Back in December, you'll remember that Sam Alvin declared an internal code red as Gemini surged on the consumer side and anthropic surged on the enterprise side.”

That led yes to a very fast sequence of new models, but also a real emphasis on codex, which has been a powerful contender and competitor against cloud code throughout the quarter.

Opin AI successfully recruited Opin Claw's Peter Steinberger to come join the company, and has been doubling down on their new focus, cutting out the side quests as CEO of applications Fiji Simo put it. Indeed, in some ways, you have Anthropic and Opin AI converging towards a similar core, despite coming at it from completely opposite sides. Opin AI had the starting point of product sprawl with a wide variety of standalone products,

that they're now merging into one super app and trying to consolidate everything under one roof, basically working inward from the edges, whereas Anthropic has moved from its single dominant product to make that core tool extensible so that ecosystem builds around it, working outward from the center. Now, a big part of the story of this quarter, as I alluded to at the beginning with Cloud Code popping the bubble, was a very different story about AI in the markets.

The big theme was the SaaS apocalypse. Basically, everywhere you looked across public software companies, there was carnage. Much of the pain happened in Big Burst as well, driven by narratives like Cloud announcing some new industry-focused feature for co-work.

“Basically, investors concerned flipped from what if AI isn't good enough to what if AI is too good?”

Satrini's 2028 research report was case in point of that. Stories of layoffs and job destruction dominated headlines, with highlights like blockcutting 40% of their staff, being read as portends for a very aggressive AI error recalibration, although, of course, as we've discussed in previous shows about jobs, there also might have been quite a bit of AI washing going on. And yet, of course, in the background, the CapEx explosion continued on a baited.

The hyperscalers expect to spend $650 billion on CapEx this year, which is three times what they were spending a couple of years ago, and even more than the inflation adjusted amount that was spent on the US Interstate Highway Buildout. Supporting the shift in focus away from the AI bubble narrative towards the What if AI is too good narrative was just the absolute monster revenue growth in so many companies in the AI space. Cloud Code as we already talked about as a standalone product went from $1 billion to $2.5 billion in about two months.

Cursor doubled its annualized revenue from $1 to $2 billion this quarter, lovable ran up to $400 million in annualized revenue, including a $100 million jump in a single month. Replet says that they're on track for a billion dollars in ARR by the end of 2026, and overall, and what will be a big part of the story, and Thropic hit a $19 billion run rate. Which brings us, of course, to the agentic enterprise, because one of the big stories and one of the big cross-cutting themes throughout the quarter was the idea of anthropic as the new enterprise default.

Based on ramp statistics, Anthropics share of first-time enterprise AI buyers jumped to 70 percent, with open AI at 25 percent and others at around 5 percent.

While Open AI's annualized revenue remains higher than Anthropic at around 25 billion, Anthropic is quickly closing the gap. Across the enterprise, we saw a shift away from pilots into production with deeper deployment depth and more focus on actual agents. Indeed, applied agentic capabilities, what companies are actually using agents for, continue to expand. Gardner is betting that by the end of 2026, 40 percent of enterprises will have working agents in production, and thanks to new products like agent credit cards from ramp and stripe, they'll be able to do more, like actually spend money.

If 2025 was supposed to be the year of enterprise AI agents, 2026 appears to be when that's actually coming true.

“I think Nvidia's NemoClaw is a case study in what you're going to see a lot of this year, which is an enterprise-grade hardening of existing agentic tools to make them viable in an enterprise setting.”

Whatever else is going on in markets, it's clear that things are changing. On one end of the spectrum, you've got a very significant increase in the number of companies listing agents as a material risk. And on the other end of the spectrum, we're seeing just how much agents can change company design. Polcia, which is a company that produces fully agentic companies, has reached six million in annualized revenue with a single founder and zero employees.

but as founder Ben Sarah put it, the zero employee company isn't a thought experiment anymore.

“It's a live dashboard with weekly metrics.”

All right, folks, quick pause. Here's the uncomfortable truth. If your enterprise AI strategy is we bought some tools, you don't actually have a strategy. KPMG took the harder route and became their own client zero. They embedded AI in agents across the enterprise, how work it's done, how teams collaborate, how decisions move, not as a tech initiative but as a total operating model shift.

And here's the real unlock. That shift raised the ceiling on what people could do. Human state firmly at the center, while AI reduced friction, surface din site, and accelerated momentum. The outcome was a more capable, more empowered workforce. If you want to understand what that actually looks like in the real world, go to www.kpmg.us/ai.

That's www.kpmg.us/ai. With the emergence of AI code generation in 2022, Nvidia master inventor and Harvard engineer Sid Pureshi took a contrarian stance.

“Inference time compute and agent orchestration, not pre-training would be the key to unlocking high quality AI driven software development in the enterprise.”

He believed the real breakthrough wasn't in how fast AI could generate code, but in how deeply it could reason to build enterprise-grade applications.

While the rest of the world focused on co-pilots, he architected something fundamentally different. Blitzie, the first autonomous software development platform leveraging thousands of agents that is purpose-built for enterprise scale code bases. Fortune 500 leaders are unlocking 5x engineering velocity and delivering months of engineering work in a matter of days with Blitzie. Transform the way you develop software, discover how at Blitzie.com. That's BLITZY.com.

It is a truth universally acknowledged that if your enterprise AI strategy is trying to buy the right AI tools, you don't have an enterprise AI strategy. Turns out that AI adoption is complex. It involves not only use cases, but systems integration, data foundations, outcome tracking, people and skills, and governance.

My company, Super Intelligent, provides voice agent driven assessments that map your organizational maturity against industry benchmarks against all of these dimensions.

“If you want to find out more about how that works, go to bsuper.au.”

And when you fill out the get started form, mention maturity maps. Again, that's bsuper.au. Most companies don't struggle with ideas. They struggle with turning them into real AI systems that deliver value. Robots and Pencils is a company built to close that gap.

They designed and deliver intelligent, cloud-native systems powered by generative and agentic AI with focus, speed, and clear outcomes. Robots and Pencils work in small, high-impact pods. Engineers, strategists, designers, and applied AI specialists working together to move from idea to production without unnecessary friction. Powered by RoboWorks, their agentic acceleration platform, teams deliver meaningful results including initial launches in as little as 45 days depending on scope. If your organization is ready to move faster, produce complexity, and turn AI ambition into real results, Robots and Pencils is built for that moment.

Start the conversation at robotsandpensils.com/aidelebrief. That's robotsandpensils.com/aidelebrief. Robots and Pencils, impact at velocity. Next up, let's get into some numbers around where practitioners on the Vanguard actually are in terms of their AI usage. This is, of course, sourced from our monthly AI usage pulse surveys.

The story is lots of usage, vibe coding becoming table stakes with more than 71% of them having vibe coded in the past month. Increase the agentic usage, where use cases that are automation or agentic rather than just assisted are up to 62% of users, and both usage and value increasing but value increasing even faster than usage.

And while users at the forefront see growing consolidation around cloud as their primary model, they are ultimately model omnivorous.

The average respondent to our surveys uses 3.5 models, meaning they are taking a portfolio approach choosing the best model for the task at hand. Between January and February, the percentage of people who had an automation use case or an agentic use case both went up, the way that we define this is an automation is AI doing a specific workflow and end, whereas a gentic AI is giving AI a goal and letting it figure out how to accomplish it. Maybe the biggest shift visible in our surveys has to do with the type of value people are getting out of AI.

When we did our survey of use cases at the end of 2025, time savings was the dominant type of value. However, in both January and February, time saving share of overall ROI went down significantly. In January, time saving use cases represented 19.9% of the use cases surveyed, and by February it was down to just 13.6%. Increased output and throughput were number one, both months, with new capabilities being number two, both months, jumping 4.6 percentage points from 22% of use cases to over 26% of use cases in February.

This is basically the shift from efficiency AI to opportunity AI exemplified in some real numbers.

Now, one of the big fallouts of the inflection point that we're living through and the new agedic era that we're moving into is that the capability overhang.

“In other words, the gap between the value that AI could be providing and the value that it actually is providing is getting more and more significant.”

I'm going to skip through a lot of the specific numbers here for the sake of making it through, but basically in every survey that you find out there, there is just a huge gap between what's possible and what's actually deployed and what's actually delivering value. What's different is that the cost of this overhang is going up as the gap between leaders and laggards gets bigger with the advent of this new capability set. Looking at a couple highlights from AI inside various enterprise functions, customer services, one of the more mature areas, with 91% of businesses at least experimenting with AI chat bots,

although there's still a lot unresolved around where customer preferences are going to fall. One survey found that 64% of customers prefer no AI in their customer service interactions,

which unfortunately for them at this point is probably just never going to happen again.

In the legal area, and Thropic Research found one of the largest gaps between tasks within AI's reach and observed adoption, arguing that around 80% of the work, AI was capable of even the only 15% of those tasks actually observed in the adoption. At the same time, dedicated tools like Harvey saw their valuations and usership go up and up and up. Finance showed one of the biggest challenges that enterprises will face this year with AI, which is access to quality data.

“The finance industry has actually been a fairly aggressive adopter, but 91% of firms report fairly low impact.”

They cite as their biggest obstacle data quality, which makes sense, given what finance does. This is hardly a finance only concern, however, which is why so much of the conversation in 2026 is about context and data. HR is one of the areas seeing the fastest growth from being previously fairly behind. One study found that HR deployment of AI had grown from 19% to 61% in 12 months, or 320% growth in a year.

It's also an area where you're starting to see some AI policy come home to boost with seven states having some sort of AI employment regulations. Sales might be the most mature enterprise AI function. In our use case research, where we organize use cases in two three categories, prime time meaning they're ready for most organizations, emerging, which means that you need some amount of infrastructure, but many organizations will find value, and frontier, which means they're valuable,

but you really got to have the right organizational setup within that framework. 63% of the use cases we tracked in sales were in the prime time category.

Finally, what makes marketing interesting as a category is that not only does it show how an existing set of functions can change,

but it shows how AI will create entirely new categories. Specifically, we're talking about the generative engine optimization, or GEO field, that helps companies figure out how to appear more frequently and more positively and more usefully in AI chatbot responses. Now, if and as user behavior shifts away from traditional search and towards chatbot related search, this is going to be nothing but more important.

Early evidence suggests that referrals from AI are not only growing, but are converting much better than traditional search, putting a lot of emphasis in this category. The market for genitive engine optimization was a little under a billion dollars in 2025, but is projected to grow to nearly 34 billion by 2034.

Now, as we start around the corner here, one of the most important things that happened last quarter,

“that was different than where AI was in the past, but I think represents AI's future,”

is that the politics surrounding AI have gotten much more pronounced and much more significant. At the heart of this was of course, the Pentagon's battle with Anthropoc. The situation ratcheted up very quickly. Reports came out that Claude had been used during the raid against President Nicholas Maduro, Venezuela, seemingly getting a bunch of people at Anthropoc angry for the U.S. government violating their terms.

That led to some tense conversations where Anthropoc wanted the Pentagon to commit to not using Claude for autonomous weaponry, or for citizen surveillance, whereas the Pentagon wanted Anthropoc to agree to terms that said they could use Claude for all lawful use. Over the course of just a couple of days, this got aggressively louder. With the fence secretary Pete Heggseth issuing ultimatums at deadlines and threatening not only to not work with Anthropoc, but to designate them as a supply chain risk, which hadn't been done to a U.S. company before.

Anthropoc did not comply, they were designated as a supply chain risk, Anthropoc sued the legal battle continued, Claude continued to be used in the war in Iran, and everything is just a mess with that situation. When Chaggy B.T. stepped in and announced that they had signed an agreement with the Department of War on the same night that the ultimatum came to pass, it did not go well for open AI. There was a 775% surge in one star reviews for Chaggy B.T.

and Claude made it to number one in the app store for the first time ever. Now, that situation is obviously far from resolved, but you can see that there is some pretty clear political resonance around these AI issues. Now, another area where AI politics grew in Stature this quarter was around the politics of data centers. We had started to get some glimpses of this towards the end of last year, as a number of smaller campaigns at the state and congressional level, began to focus in on data center related issues.

Ultimately, this led to President Trump, getting all the hyperscalers to agree to promises,

In the U.S., the anti-AI movement, which isn't really a movement, but a collection of people with different grievances,

“went mainstream enough that it made it to the cover of Time magazine with their people versus AI cover.”

Now, towards the end of the quarter, the White House released its legislative framework, which should be seen as an opening salvo in a heightened state's conversation around AI policy. Now, whether there will be any room to actually debate AI rules when we are at the time of recording this episode, still living inside foreign wars, government shutdowns, airline accidents, and three-hour TSA lines, I'm not really sure, but I do know that heading into the midterms, AI is going to do nothing but grow in significance as a political issue.

Summing it all up, the story heading into Q2 is that this is one of the most exciting, but also most destabilizing transitions we've ever seen.

“To take an example of just one weekend in March.”

Andre Carpathy's posting of a job visualization of LLM's rating of AI exposure to different jobs, caused a wave of panic. That coincided with rumors of 20% layoffs at Meta, and Bernie Sanders posting videos of him talking about Epting about experts on Twitter. While at the meantime, we had an Australian man using AI to help design a cancer vaccine to cure his dog,

hundreds of articles all over X about people's 12-age and orchestration teams,

and a never-ending drug-beat of new features, and new models increasing our capabilities.

In short, the discourse everywhere is at 11. Now, as we move into the next quarter, some things to watch. First of all, from a competitive standpoint, it's clear that it's no longer just about the model, but about which agent platform people are using their models in. The most interesting battle of this quarter was not actually GPT 5.4 versus Opus 4.6,

which was Claude Code versus Codex versus OpenClaw. Fascinatingly, as that competition happens, we're also seeing a convergence where every AI product becomes every other AI product. Levelable and replete which had previously been vibe-coding platforms both announced a wildly expanded set of features this month. Claude Codex and OpenClaw all got closer and closer together. Products like Proplexity, Computer, and Notion Custom Agents all started to nudge into the same space.

As Peter Yang put it, code is the foundation of all knowledge work. If an agent can write code, it can also generate apps, presentations, animations, and more. When it comes to how enterprises have to deal with all this, one thing that's clear is that time savings ain't it. And the thinking about new capabilities is going to be much more profitable.

“Unfortunately for companies that are behind, I think that the gap between the leaders and laggers is going to do nothing but increase right now.”

In other words, the capability overhang is going to widen before it closes. Now, on the flip side, the companies that can be on the leading side of that are going to see extreme compounding gains, creating a very strong incentive to get there. It would be hard for the next quarter to have as much raw change and as much raw recognition of changes we did in the last one.

But with AI, you never know. For now that it's going to do it for today's episode of the AI Daily Brief,

looking forward to being back with you with more build week episodes this week. Appreciate you listening or watching, as always, and until next time, peace. [Music]