# What the Top AI Users Are Doing Differently — Transcript (2026-08-25)

https://aidailybrief.ai/e/2026-08-25 · Listen: https://pod.link/1680633614

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[00:00:00] There's always been a gap between an average AI user and the most advanced AI users, but my goodness has that gap grown In recently released research, OpenAI showed that the gap between the most advanced users and the average AI user had grown from 2.6X back in January by the end of June In other words, the most advanced users of AI were using eight times as much AI as were their average counterparts. The reason, of course, is agents At the beginning of the year, agentic use cases became viable and significantly upgraded the difficulty, complexity, and importance of the work that AI could take on The top users have jumped in headfirst, figuring out how to significantly increase the value they get from their AI usage

The average users, on the other hand, just haven't but as the power users use agents to take on increasingly valuable work, That gap is just poised to grow

The 

AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. [00:01:00] All right, friends, quick announcements before we dive in First of all, thank you to today's sponsors, KPMG, Blitzy, Harbor, and Hyperagent. To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts

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According to roadmap documents viewed by The Information, Meta is putting the finishing touches on their consumer agent ahead of release in the coming weeks

Now, this is something we've been hearing about for a while, but we're getting more [00:02:00] details as the product becomes imminent. Internally, the product is known as Hatch

And sounds like it could be sort of in the Grokbot family of delivering a more streamlined version 

of an 

OpenClaw of an OpenClaw-style agent experience. The company is reportedly looking at, using Hatch as part of a new AI agent subscription, which could justify a two hundred dollar a month price tag for high usage accounts 

Meta Meta also plans to launch a new platform on WhatsApp to allow better integration for third-party agents. The platform will reportedly allow multiple agents to coordinate with each other using WhatsApp messages. again, which is a mirroring some of the functionality, like I said, of Crockpot



For what it's worth, this doesn't strike me at all as Meta cribbing off of GrokBot. I think these are just interaction patterns that we're likely to see more of.

The The rollout could begin as soon as this week as a preview to a smaller group of customers. finally, in Meta model news, a larger model known as Watermelon is being prepared for an October launch

Back in July, AI CEO, Alexander Wang, told staff that Watermelon had already caught up with [00:03:00] GPT-55 on internal benchmarks. Now, obviously, the frontier has moved forward substantially with the release of GPT-56 

56 56 

And we'll likely move again by the time October arrives



So So we'll see whether watermelon can actually keep pace Or Or continues to fall in the column of Meta getting closer to the frontier without actually reaching it

it Speaking Speaking of GrokBot, one of the barriers for a lot of you guys testing it hasbeen its extreme premium pricing



In in fact, it wasn't just high pricing, it was kind of confusing pricing





initially users weren't sure if they had to subscribe to both Cursor Ultra and SuperGrok Heavy, which would be a total of $500 a month to access the service. but but then many, myself included, were able to get it just through SuperGrok, which is itself not a cheap subscription



but but it was very clear that this was an intentionally rate-limiting launch to make sure that things didn't go down

with the hopeful anticipation that prices would be reduced later As As of this week, Grokbot is included in the $60 a month Cursor Pro subscription, as well as the $100 a month Super Grok subscription

Speaking Speaking of dropping prices, OpenAI is also dropping [00:04:00] prices for GPT-5 six Sol. Accessing Sol over the API will now cost $4 per million input tokens and $20 per million output tokens, down from $5 and $30 respectively. Costs for Luna and Terra werealready cut late last month



many many are speculating that this is OpenAI trying to put pressure on Anthropic ahead of their IPO

My guess is that for whatever ancillary benefit that might have



OpenAI is more likely to just be realizing

that they've got a new set of challenges based on what we talk about every week here on this show That especially business customers are not just going to use the most expensive state-of-the-art model for everything anymore and have to think in more sophisticated ways about their complete model stack.

To the extent that OpenAI has the compute to deliver their frontier models cheaper

It seems like they've decided it makes sense to do so

do so. Now Now following up on news from yesterday

Business Insider reported that Hugging Face was courting an acquisition at a $13 billion valuation. And according to sources speaking with the information, part of what might justify their high asking price is that the company is now generating more than 150 million in annualized revenue, which [00:05:00] is up 50% from two months ago Now that number may seem low relative to, for example, the coding agent startups But Hugging Face is a company That has specifically not been focused on generating revenue. Ninety-seven percent of users access the platform entirely for free, including downloading the latest model weights. The primary revenue drivers for Hugging Face are premium and enterprise-grade accounts serving inference and partnerships with hyperscale clouds.

Essentially, up until now, the profit-seeking segments of the platform have existed to subsidize the free hosting and distribution of open models

In June, CEO Clem Delang said that the number of premium accounts had doubled in the first half of the year, and based on that, the platform was approaching profitability

For acquirers, this is very likely not a strict revenue multiple type of conversation. Summing up the logic, Jess Fields writes: " Hugging Face should be worth as much as Cursor is, way more than 13 billion, maybe three to four times that. Considering that Hugging Face is the backbone of the open weights challenging frontier gatekeeping, it occupies a uniquely powerful position in the entire economy



Now, one of Now, one of the companies that people are speculating on might [00:06:00] be an interesting fit for Hugging Face

is of course NVIDIA. If for no other reason than they seem to be in the conversation for every acquisition right now



in fact, with a flurry of reporting around NVIDIA's deal-making in recent days, some are wondering just what Jensen is building. Over the past week, we've heard that NVIDIA signed a deal to license technology and acquire talent from Poolside

buy a stake in data labeling company Mercur and potentially invest in Perplexity at a potentially perplexing thirty billion dollar valuation. That's to say nothing of other equity investments in NeoClouds, land and power deals, and data center backstops

Some have started to conceptualize NVIDIA as the central bank of compute, standing behind the AI economy, as well as setting the price of the key resource. Martin Peers of The Information compared Jensen's approach to that of John Malone, who built up a giant cable TV empire in the 1980s



another rough comparison point might be Google's approach with their holding company Alphabet in the mid-2010s when Google restructured the company and created their Other Bets division to house moonshot investments in Waymo as well as Google [00:07:00] Ventures. The basic idea was to reinvesttheir massive earnings from internet advertising into the broader tech ecosystem

and of course, subsequently, a lot of those bets have now paid off Nvidia's Nvidia's approach is obviously different. Rather than fanning out across next generation tech, Nvidia is sticking to the AI ecosystem. Still, they are building up a formidable portfolio of other bets.

During their last earnings call in March, they reported $42.3 billion invested in private companies, 

And the number will certainly be higher when they report later this week



and And before we scream up and down shouting circular deal-making, one thing that's important to recognize is that part of this is a reflection that Nvidia is more or less tapped out when it comes to reinvesting in their own business. Nvidia does not operate their own fabs.

So at this stage, growing chip revenue is limited by constraints across their network of suppliers In other words, constraints they can't control. By turning their investments outwards, NVIDIA supports the entire AI economy, and that in turn ensures that their revenues can stay strong for years to come.





at

at least that appears to be the goal as they get even [00:08:00] more ambitious with their other bets



rema-- Still Chips remains the big game And over on the other side of the world, Taiwanese prosecutors have charged nine people for chip smuggling, including one NVIDIA manager. on Monday, the Taiwanese announced nine indictments in relation to a scheme to smuggle cutting-edge Blackwell 300 systems into China.



in in addition to the person who was identified as a manager in NVIDIA's distribution business, two others worked for NVIDIA partner Supermicro. Earlier this year, a Supermicro co-founder was charged in relation to another smuggling incident. Both NVIDIA and Supermicro have indicated the problems are contained to a few rogue employees and that they're working with authorities



to give you a sense of the scale of this incident, the group allegedly ordered a hundred and thirty Supermicro servers containing Blackwell 300s With prosecutors claiming that 74 were delivered to buyers in China, while another shipment of 56 were stopped by Taiwanese officials

In In total, you're talking about less than 10,000 chips, which is not nothing, but certainly not enough to build a frontier training cluster

Lastly Lastly today, an interesting peek under the hood Around how much AI one hyperscaler's employees are [00:09:00] using 



Business Insider got hold of an internal spreadsheet where Microsoft employees self-report key metrics including salary, bonuses, and AI usage

Now, this is not a story about token maxing, as BI found no correlation between token burn and financial compensation or promotions at Microsoft

Both across and within different departments, AI usage was extremely jagged

In the Azure department, the range of monthly AI spend was a dollar to seventy-five hundred dollars In the cloud and AI division 



the top end went all the way up to $15,000. And in Microsoft Customer and Partner Solutions, there was at least one person who ran up a bill of $28,000 The The median AI usage across all the different departments was a little more clustered together

Seven of the eight had median AI usage

of right around 150 to $500, with Core AI being the outlier where their median AI usage was $975



Now Now importantly, this is all voluntary self-reporting. The spreadsheet is maintained to allow staff to voluntarily share compensation figures In an attempt to [00:10:00] promote pay transparency

Only Only a tiny sliver of Microsoft's 223,000 employees overall contribute to this chart Just 600 US employees, in fact, only 350 of whom reported their AI usage



still it gives you a sense of the type of magnitude you're seeing

among perhaps the more prominent AI users inside a company like Microsoft, which I think is actually the perfect segue into our main episode, which we will begin now A new study from KPMG and the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than five hundred early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs.

These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI. Learn more about what separates AI amplifiers from everyone else at [00:11:00] kpmg.com/us/aiamplifiers. 

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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 [00:13:00] users get 1000 in inference Forget local agents and chat workflows waiting on your laptop to be prompted Hyperagent deploys alwayson 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 1000 in inference at hyperagent.com/aidailybrief. 

Welcome back to the AI Daily Brief

One of the best things that's happening right now when it comes to AI narratives at least, is that we're starting to see a shift away

From the accepted without question kind of premise that AI is obviously going to be job destroying

Now, regular listeners will know my position on this is pretty clear

I am not Pollyannish at all about the [00:14:00] potential scale of challenge when it comes to the transition between two totally different work paradigms. You are inevitably going to see certain types of roles

that this new category of technology will obviate the need for



that will cause real personal disruption, that that society would do well to be ready to support the people affected.

The The idea, however, that there was going to be some radical and rapid jobs apocalypse

was never accurate



on on this point, Sam Altman has increasingly gone out of his way to not just have changed his position, but to explain that he believes that he was wrong, and to try to explain why he thinks he was wrong.

In a recent podcast interview, he said, " I thought when we got to GPT-4, which was back in 2023, that very quickly after that there was going to be much more disruption, software businesses up for grabs right away than it turned out to be. I think I was wrong about a few things, but one, in terms of the speed.

The economy just has so much inertia. People keep doing the same things, buying from the same company, wanting to use their tools the same way. I think this is [00:15:00] actually a positive in many ways, and it's going to make this big transition go smoother and slower. I'm grateful for it, but it means we've all been too ambitious on timelines.



this, even with this incredible technology, society and the economy will adapt more slowly." In other words, as I like to think about it, who needs a pause AI movement when you've got corporations?

What's interesting though is that Sam actually went farther





and recognize that while yes, institutional inertia is perhaps the, biggest driver of the slowdown in the rollout 

That 

that inertia happens on an individual level even with really advanced users as well In In that same interview, he said, The thing that feels most psychologically inconsistent about myself is that I have for 20 years been using computers the same way.

I now have a magic thing called Codex. So do you, so does everybody. that means I should completely be using my computer in a different way. I should not be clicking around, pasting from one messaging app to another. I should not be scrolling mindlessly through my emails trying to figure out which one is the least painful to open.

I should not be keeping a to-do list and doing these rote computer tasks the same way [00:16:00] I have for so long. And yet there's something in my mind that is encoded, that doing this kind of stuff is what it means to work and be productive. If you asked me, I would never say I like doing it that way. In fact, I'd say the opposite, and I'd mean it.

But by revealed preference, I have a better way to do it now, and I still do it the old way. It makes no sense other than I must secretly like it or feel good about it."

it." I don'tI don't think it's even some secret satisfaction though. I just think we all get stuck in the patterns of how we've always done things

We build intellectual mind muscle memory

And the thought of undoing that to go build a new type of mind muscle that is much less comfortable often appears exhausting when we know exactly how long it will take us to do it the old way



and we could just get that done right now and move on to whatever it is we actually wanna be doing. it's so which is why it's so important to spend time looking at how people who have broken out of their mind muscle memory are doing things

And interestingly, a couple of weeks ago, OpenAI published some research about exactly that. Now, Now, I don't know why they weren't screaming about this article from the [00:17:00] rafters, but if I miss something that's putting numbers around how frontier firms are doing things differently than others, you know that thing was not promoted nearly widely enough 



in any in any case, I only noticed the data when A16Z reposted it as part of their charts of the week

The chart that grabbed mine and many others' attention

was this one showing Codex user growth by enterprise job title. it was indexed back to the beginning of February of this year, and in that time

While every role has gone up

It is the non-technical roles that have grown the fastest. Now, Now, of course, part of this is because they had a lower starting point. But to give you some examples while while use among engineering and technical practitioners is up 5X in that time Use in finance and accounting is up 20x In marketing and communications, it's 26X.



In people and recruiting and separately sales and account management, it's up 41X. And in legal, 

Codex usage is 108X from where it was back in February

Joking but not really joking about the lawyer side of this.



Spellbook Scott Stevenson quoted Jack Newton saying, " LLMs are for [00:18:00] lawyers what spreadsheets were for accountants."

But that But that was hardly the only data that was available here.



the big through line signal in this piece, which was called Enterprise Signals: What Frontier Firms Are Doing Differently was two parts. First, more work is being delegated to agents. And second, because of that, there is a compounding effect where the firms that are farthest along are getting farther away from the firms that are behind



In other In other words, agentic use compounds their lead



and importantly, this is not a model question

It's about, as OpenAI puts it, how they put those models to work, moving from assistance to delegation, giving agents the context and tools to complete complex tasks, and accelerating agentic beyond software development



so let's so let's talk about a few of the most interesting numbers



a a year ago at this time in August of '25, when GPT-5 was announced







the balance between ChatGPT ChatGPT usage and agentic usage 

as as measured by the output tokens produced by the enterprise interacting with the ChatGPT ecosystem in any way [00:19:00] basically 100% ChatGPT tokens and not agentic tokens



Between October and February, the first glimpses of the actual agentic era started to be seen

Codex becomes generally available in October, and a low single-digit percentage of enterprise output tokens are now in that agentic category

In In December, GPT 5.2 comes out and we see a bit of a bump that extends up into the beginning of February



app launches for macOS





the percentage balance between ChatGPT and agentic usage, again, as measured by enterprise output tokens, was 87% ChatGPT to 13% agentic



But then from there, the agentic use cases just take off

We get to March. Codex is released for Windows and GPT 5.4 comes out And we're now at 73% ChatGPT, 27% agentic

ChatGPT, 27% agentic

in Towards the end of April, just a month later GPT-5 five comes out and we get the flippening where all of a sudden agentic 

output tokens are representing fifty-three percentAnd why, And by June when this

data set [00:20:00] ends, were down to thirty-six percent, while agentic tokens were up to sixty-four percent Nownow keep in mind, this does not mean that all of a sudden 64% of the times that someone sits down to use an OpenAI product at work, they're doing something with agents

Instead, Instead, what this means is that if you use the amount of output tokens that the aggregate set of prompts lead to. In other words, if you use output tokens as a proxy for the amount or volume of work being done, the preponderance of it, almost two-thirds by the time these statistics were captured is now being, agentic work



agentic, so point one was that agentic use is up, and point two

was that the gap between the leading AI users, i.e. the ones using agents the most and the best, 



and the general enterprise users was getting wider OpenAI defines frontier firms . as those in the top 10% of usage in a month as measured by output tokens per active user

With the average firm to be between the 45th and 55th percentile



and providing the best numerical advice 

you can see that going back [00:21:00] to about April of 2025, throughout the year of 2025

The gap in output tokens per active user between the typical firm and the frontier firm was only about two X. as in the average user at a frontier firm used about twice as many output tokens as an average user at an average firm

firmthe gap started to widen around October, and in January stood at 2.6X The gap The gap now has absolutely exploded



with with the distance between the typical firm and the frontier firm now at eight point three X Overall, while the average firm is using about twice as many tokens as they did a year and a half ago, frontier firms are using 17 times as many tokens as they were a year and a half ago

Now, part Now part of this is of course that they're just using agents more, but part of it is that they're also better at using agents



To To measure this, OpenAI looked at the number of weekly active users who use either plugins or skills



plugins are, of course, capability sets that can connect to other applications or data, whereas skills are [00:22:00] reusable instructions that can help with common workflows 



At At typical firms, about 9% of weekly active users are using plugins, and only about 3% are using skills At frontier firms, which is again the top 10% of enterprises, 19% are using skills and 21% are using plugins





which which is not to say that those frontier firms have topped out in terms of their usage. By way By way of comparison, at OpenAI right now, 93% of their employees are using skills and 95% are using plugins



but but still coming back to the gap between employees at Frontier and typical firms, You're talking about two and a third times as many people using plugins, and more than six times as many people using skills





and in terms of what work they're deploying this towards, as we saw from that chart that kicked off this show

the fastest growth in these agentic use cases is coming from knowledge workers that are outside software and engineering functions

OpenAI OpenAI diagnoses it like this: Software, they said, moved first for a reason. Code bases give agents clear context. tests make outputs easier to verify, [00:23:00] and progress in coding helps accelerate AI research and development

In In contrast, they say, progress in general knowledge work has been slower because many tasks provide limited context, can be difficult to specify, and lack clear criteria for verifying the result. But But continued scaling advances in reinforcement learning and targeted efforts to improve performance on evaluations such as GDPVal are bringing more real-world tasks, tools, and work environments within the reach of frontier models.

As As a result, agentic AI has increasingly found product market fit with general knowledge workers since the beginning of the year.

Look, Look, it is great that OpenAI is working hard to have their models and harnesses work better for knowledge work. But stuff that OpenAI has done is not the reason



that agentic use has grown among these non-software engineering knowledge workers? The reason that agentic use has grown among general knowledge workers is that we've started to figure out the patterns

that actually allow agents to thrive in our own contexts.



So what are those patterns? From this, I'm combining 



the research from OpenAI about the types of use cases both in chat and agentic [00:24:00] across departments

plus plus our own experience at both AIDB and at Superintelligent

To provide a little bit of a picture

of what that more advanced agentic use actually looks like in practice



you can see in OpenAI's chart That there's a massive shift in the type of work when you move between chat and agentic And to be clear, this doesn't mean that the type of work being done with chat is not valuable. support for writing and communications, knowledge retrieval and search.





those things do bring a lot of value



But But agentic takes a lot of those individual workflows and instead moves them into systems-level work that can impact more than just the individual

You can You can almost think about a use case ladder. at the base is generation. Think drafting an email, a report

An Excel formula, things like that. on the second rung is synthesis, being able to take disparate data sources and produce something more complete that is informed by them. moving farther up the ladder, we have execution, where the AI is actually being tasked with interacting with existing systems and doing things within them 

Which of course is closely tied

[00:25:00] to the next level of maintenance



where agents are tasked not just with executing something specific, but maintaining a system over time



So So for example, if you're looking in legal, the context that people are drawing on is of course things like contracts Policy documents, precedent



as well as any relevant counterparty history the types of agentic work patterns you're gonna see are around things like comparing terms, flagging deviations, drafting red lines 

recording decisions, monitoring commitments



humans will continue to negotiate the material terms, to set risk tolerance, to approve exceptions and final language So basically you have a division of labor where the agent is handling coverage and coordination

While people still own risk judgment and accountability

If If you go back and look at the difference in the categories of work in the legal field that are being done via chat or agentic



writing makes up a full 57% of legal work in chat, followed closely by knowledge retrieval at 20.5%.



at 0.2%.



now now you move over into agentic And you've got writing down [00:26:00] to knowledge retrieval down to 8.3% And a whole bunch of new categories coming online in a huge way. Classification and extraction jumps to four point six percent

System 

operations jumps to 17.7%. Workflow automation jumps to 7.7%.

And coding, actually building applications, even though these aren't the software engineers, jumps to 32.9%. And you And you see this pattern in basically every other department as well



Writing 

and knowledge retrieval with a side of education and guidance remain the preponderance of chat tokens



while while Agenta gets into deeper systems integration work

work Now, one Now, one thing that I think is going to supercharge this to the next level is the emergence of multiplayer and team AI as opposed to just individual AI. I think I think that even though you are seeing these frontier users move more into these higher order tiers of execution and maintenance of systems, oper-- Most of these agents are still operating within individual silos. and my belief is that where a lot of the next generation of gains are going to come from is actually at the intersection of different teams

Ultimately, Ultimately, none of this is [00:27:00] all that surprising. it has been clear for a while that 2026 was the year that agents became real But But boy, the numbers do not lie

And and while even the frontier firms are still just barely beginning to figure it out, the fact that they seem to be racing ahead

and putting more and more distance between themselves and the average firms should be a wake-up call for those who aren't deploying agentic uses at scale yet

ThisThis is probably where I should insert a shill for our training programs over at Superintelligent. But if you are a regular listener, you will already know thatthose are there and available for you you In any In any case, this is great stuff from OpenAI.

Please keep doing this, and please promote it more heavily next time. And of course, for you guys, appreciate you listening as always. Until next time, peace. 

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