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Today on the AI Daily Brief, six questions Shaping Enterprise AI before that in the headlines, Sam Altman goes to Washington and the conversation has gotten a lot more complicated over the last week. 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 Blitzy, Retool and Airtable. To get an ad free version of the show, go to patreon.com aidaily brief or you can subscribe on Apple Podcasts. To learn more about sponsoring the show, send us a Note@ SponsorsIDailyBrief AI and a bone up on your AI skills with the last month or so of summer, go check out our latest free self directed education program, this one being a choose your own summer adventure. You can find it at summeradventure AI well, Sam Altman has arrived in Washington to meet with lawmakers and White House officials. And when the trip was set at the beginning of last week, the agenda was pretty simple. Altman would brief Washington on the capabilities of OpenAI's new model and discuss a protocol for release, hopefully avoiding a repeat of the Fable and GPT5.6 rollout. Since then, however, we've had the OpenAI hugging face hack, a public debate about Open Weight's models, and an attention getting petition for the government to step in and build the capability to slow down the pace of Frontier AI. In other words, conversations have become a lot more complicated for Altman in just a couple of weeks. According to reports, Altman met with Senate Commerce Chair Ted Cruz and several Democrat senators on Wednesday, but we got very little information on what was actually discussed. Speaking to reporters, Altman declined to state when or even whether the model being previewed would be released, commenting, not sure that's the part we're here to talk about. Altman also declined to discuss the new capabilities of the model that give cause for concern. Now, of course, the hugging face incident looms large over this visit, but it increasingly appears like the model at the center of that controversy will not see release. In a Tuesday update to their postmortem blog, OpenAI said that the model was an internal only research prototype never intended for public release. In Washington, Altman told the press that the model has now been permanently deactivated and is inaccessible even for internal research, meaning presumably it's not the model being previewed to lawmakers this week. Now, Sam said that he and Ted Cruz had not discussed specific legislation, but that quote, we talked about our new model and what it's going to take for America to remain competitive with AI. Altman also said that he didn't support mandatory safety testing, particularly because it could introduce an unnecessary burden on Open Weight's model developers, but added for frontier models at new levels of capabilities, we think it's really important that the federal government has great testing capacity and capabilities. Altman said he plans to meet with a range of other officials to end the week, including White House Chief of Staff Susie Wiles, who for whatever reason has wound up as one of the key decision makers on AI policy. That meeting will likely include a discussion of the voluntary AI safety testing framework, which has a deadline of August 1. Reports state that this framework has been circulated to OpenAI, Anthropic and Google for comment, but Altman declined to comment on the draft. In a hallway interview on Capitol Hill, Altman was asked whether he would talk to the White House about the need to decelerate AI development. Representing the views of his staff from the recent open letter, Altman responded, I wouldn't use the word deceleration, but we talk about the need to pace it as the models get more capable, which I think is in everyone's interests. Now. One other story that I'm going to get into in more depth tomorrow is is the significant increase in revenue numbers on both the OpenAI and Anthropic front. But I do not want to bury that in the headlines. So that will be a major topic for tomorrow. Come back for that. Suffice it to say The CFO of OpenAI, Sarah Fryer, recently told employees that annualized revenue in July topped all of the previous quarter. One more bit of OpenAI intrigue President Greg Brockman says that the company is working on an entire range of devices to give a physical presence to their chatbots. In a new interview with former Wall Street Journal reporter Joanna Stern, Brockman confirmed that OpenAI's hardware plans are still on track, stating that the company is building a family of devices. He wouldn't confirm the recently rumored smart speaker or any other form factors that have seen speculation this year, nor would he give a timeline beyond commenting. You can expect them soon. Still, this is the clearest confirmation we've had so far that a full hardware range is still on the roadmap, surviving the end of SideQuest and an IP lawsuit from Apple. Brockman was understandably brief when talking about that lawsuit, stating, we are focused on our own development and technology. One interesting bit of competitive news, which I think sounds good for consumers, particularly those of you who are in the enterprise without a ton of choice on which models and platforms you're going to use. Microsoft appears to be gearing up to compete more directly with OpenAI and anthropic with the development of a Copilot super app. During Wednesday night's earnings call, CEO Satya Nadella confirmed the app is coming later this year with the goal of unifying the Copilot experience for both consumer and enterprise customers. He said Copilot is rapidly evolving from chat to cowork to autopilot. This quarter we are bringing these Copilot experiences together, including code in one super app. This is a major step forward and I look forward to sharing more soon. Microsoft is beginning to see OpenAI and Anthropic as direct rivals thanks to the capabilities of their new MAI models. Nadella told analysts that the combination of cost and data privacy concerns gives Microsoft an opportunity to sell customers on their own, cheaper models. When asked about the rolling debate about open versus Closed, Nadella suggested the framing is too simplified. He said the goal is to have the firm be in control of their own destiny. We are very, very clear about the architectural design of the platform, which is you get to keep your harness separate from the model. That means any model at any given time is swappable. Now, I'm sure some of you will think that that's kind of a corporate answer, but I actually think that his assessment of how most enterprises feel is correct in that I don't think that most enterprises actually care ultimately about whether a model is open or closed. They care what they can do with it, what control they have, and what sacrifices around control they're making to someone else to have access to the systems they're using anyway. Overall, Microsoft is increasingly positioning themselves not as a reseller of OpenAI or anthropic products, but rather as a model agnostic platform offering a full range of options, said Nadella. Every customer wants the right model for each task based on latency, quality, cost and compliance. We offer the broadest model catalog in the cloud with over 11,000 models, including the leads from OpenAI, Anthropic, Mistral, Xai as well as our own MAI family. Now, along the swirl of all these big discussions and jockeying for position in AI, Mark Zuckerberg has made the case for AI acceleration in a new op ed in the Wall Street Journal. In an essay titled the AI Future is for Everyone, Zuckerberg argued that the defining question of the AI age won't be whether superintelligence will exist, but who will have access to it. In other words, whether we end up in a world where superintelligence is closely held by a handful of institutions or broadly distributed to normal people, zuckerberg wrote. It is surprising that the discourse from many of those who are developing artificial intelligence is so filled with doom. I don't understand why anyone who believes that AI will eliminate most jobs and much of humanity's relevance would rush to build that future. This is, for what it's worth, exactly the point that I was trying to make yesterday when I was discussing what I think the normie response to the pacing the Frontier letter would be that the only acceptable answer to why are you building AI? Is not well, if we don't, someone else will, but but instead because we think AI will be awesome and dramatically better than all the risks that it comes with, zuckerberg continued. The notion that AI is so dangerous that the only safe path is an extreme concentration of power seems dangerous historically, hoping that an absolute power will benevolently provide for humanity if sufficiently enlightened hasn't led to safe or positive outcomes. Zuckerberg's view is that much like previous technologies like the Internet, the best result will come from diffusing the technology freely across society, he wrote. Rather than centralizing this power, we believe that delivering personal superintelligence to everyone is the way to answer this question. This has the potential to begin a new era of personal empowerment in which individuals have greater freedom to pursue their interests and reach their full potential now. The op ed came as part of a press tour that linked up with Meta's new AI optimism campaign. In a separate interview with the Journal, Zuckerberg called for the US government to accelerate AI development rather than restrict it. He argued that the benefits of broadly distributing AI outweigh the risks by quite a margin, adding, I get that it's always hard to debate about the future because it hasn't happened yet, but I do think we have a lot of data points at this point, and that should point us to be much more optimistic than I believe the current discourse reflects. Specifically, he warned against thinking a 30 or 60 day government review window is harmless, commenting, the field is moving so quickly, that actually is quite a meaningful amount of time now. Notably, Meta is the only frontier AI lab that hasn't agreed to the government's voluntary testing framework. Zuckerberg also said that the US government shouldn't ban Chinese AI in a separate interview with the Financial Times. Not only does he think a ban won't be effective, but he believes it would open the risk of regulatory capture and could stymie the release of open models more generally now met as AI CEO Alexander Wang recently said that the company will begin launching open source models again, suggesting that this isn't just hollow sentiment. Still, overall, the core message is simply that more AI optimism is needed. Speaking with the New York Times, Zuckerberg said, so much of the discourse from a lot of the other labs that are developing this is overwhelmingly filled with doom. There needs to be a voice or several voices that are bringing realism to this debate. Now. I think. Unfortunately, Mark Zuckerberg's power to be the leading face of AI optimism is limited by history and people's fairly negative view of the overall impact of social media on society. Still, to start to have loud, sustained discourse that other people can pick up and run with is immensely important. And you better believe I will be here amplifying that message. For now, however, that's going to do it for today's headlines. Next up, the main episode. One of the most important AI questions right now isn't who's using AI? It's who's using it? Well, KPMG and the University of Texas at Austin just analyzed 1.4 million real workplace AI interactions and found something surprising. The highest impact Users aren't better prompt engineers. They treat AI like a reasoning partner. They frame problems, guide thinking, iterate, and push for better answers. And the good news? These behaviors are teachable at scale. If you're trying to move from AI access to real capability, KPMG's research on sophisticated AI collaboration is worth your time. Learn more at kpmg.com us sophisticated that's kpmg.com us sophisticated Blitzy's deep code based understanding unlocks the thing every roadmap owner cares about shipping new features. Here's the truth about building inside a massive enterprise code base. Writing code was never the bottleneck. Context is which system does this touch? Which contracts can't break? Which standards apply? Blitzi already knows because it reverse engineered your entire codebase into a dynamic knowledge graph before feature work began. With that complete picture, Blitzy builds features end to end architecture, APIs, UI and tests, all validated against your existing systems. One blitzy customer built an AI native application from scratch with 100% autonomous completion, saving over 2,700 engineering hours. Features that respect your code base instead of fighting it. Stop letting your backlog grow faster than your team. Accelerate your roadmap@blitzi.com that's B L I T Z D Y.com this episode is supported by Retool. AI made building software easier than ever, so more people are building it than ever, usually without a thought for security. Right now, people in your company are vibe coding, and every ungoverned app that touches your data is a risk you own. Retool takes that risk off your shoulders. Build apps however you want, natively with Retool or with Claude Code, Codex or any coding agent, and ship it in a secure, governed environment. Security lives in the platform, not in each app, so however it was built, it's governed the moment it ships. It's why teams at Amazon, Stripe and Brex build on Retool, and new enterprise customers who Sign up by September 30th get up to $10,000 in AI credits per year. Learn more at retool.com aidaily 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@hyperagent.com AIDAILY Brief welcome back to the AI Daily Brief. This week I had the chance to be out in Utah with KPMG for their annual Tech and Innovation symposium. Now this is the second year that I've been at the event and in each case have had a chance to do a similar type of presentation. This time around, both my focus and the focus of conversation after was about trying to sum up, broadly speaking, the big questions that are currently shaping how enterprises have to think about AI. And what's extremely notable to me is how much the conversation has changed since last year at this time. Now I'm going to go through a version of the presentation I gave, but before that I actually want to zoom back to last year. Last year I did, where AI is 15 slides and 15 minutes, which was actually, as you can see, 23 slides and looking back, it's almost quaint. What we found interesting or fascinating and what we were discussing at that event. The first theme was acceleration, and we talked about how AI wasn't just moving faster, but was actually getting faster in the speed it was being adopted. I discussed the more than 100% growth in the total monthly tokens that Google was processing between May and July, where they reached nearly a quadrillion tokens. Now, as many of you know, a quadrillion tokens at this point is about what a single open claw left unattended will do in a month. But it was a big deal back then to see this massive inflection point, and indeed some of the themes from that presentation were effectively setups to where we are now. The compute shortage has done nothing but get worse as we've moved to a new era. And of course, even back then the big conversation was agents. Now, what was interesting is that, at least in the way that we use agents today, agentic AI was still firmly in the domain of the future. This was The Claude Force Sonet03 type time horizon, and we were just wrapping our heads around the meter time horizon task graph that showed that AI capability was doubling every few few months. Now, speaking of themes that would continue to be important even back then, it was clear that agentic coding was the breakout agentic use case. Then again, to give a sense of just how long ago this was, we were all gobsmacked because we had hit a billion dollar revenue run rate in just about a single year. To put a fine point on how much this has changed, right before this I read a post from Dwarkesh that suggested that Anthropic could get to 100 or 150 billion revenue run rate this year. Now I won't go through all of these different slides, but what stands out to me reflecting back then was that the questions of AI and agents were really still for some if questions. One slide that I didn't have in this particular chart, but I know I had in a longer presentation that was being given around the same time, was this chart from McKinsey that showed the growth in the number of organizations that had implemented at least one or two or three different AI use cases. Yes, the big deal in mid-2025 was still that something like 40% of enterprises were up to two or three use cases a year on. The conversation has changed immensely and the historian in me thinks it's worth reflecting how we got here. The big capability jump, as we now know, came towards the end of the year, the November December time period where we got Opus 4.5 and GPT 5.2. For whatever set of reasons, those were the model updates where agents and agentic workflows actually came online in a major way. Now what was fascinating is that it actually took a couple of months for people to really grok that something had shifted. Everyone went home for the holiday, had a little bit of time to decompress, and when they fired up their instance of Claude code or whatever tool they were using, they found that the stuff that they could do was significantly different than what it had been before. I still remember vividly the absolute tidal wave of tweets in that week between Christmas and New Year's, of entrepreneur after entrepreneur and developer after developer coming back gobsmacked about what they could now build that they simply couldn't before. Now, what's interesting is that this almost immediately translated into organizational practice as well. Part of this was because software organizations had been adapting to greater and greater capabilities throughout the year and even before. By the turn of 2026, we were long past software engineering organizations viewing AI coding as just an autocomplete solution. And they became some of the first groups in the enterprise to actually shift from viewing their job as writing code to managing the agents that wrote the code for them. That said, maybe because the enterprise folks had been paying attention for over two years, at that point, it wasn't like there was some major lag from the AI early adopters to enterprises thinking about what this new agenda capacity was going to mean for their work and coming back into 2026. It was absolutely not just software engineering organizations that were racing to put into practice these new ways of working. You saw vanguard builders and early adopters across domains from marketing to legal to finance, starting to figure out how to bring this new capabilities into their work as well. Alongside the model, jump folks also recognized that part of the new capability set was actually about the harness that you situated the models. Now, Claude code had been growing in adoption throughout 2025, but became a real focal point in the new year, which was perhaps augmented by OpenAI going all in on their Codex product as well. Still, I think in many ways where this whole idea of harnesses and frankly a much deepened understanding of what we actually mean when we say agents and what it means to build and manage an agent, came when OpenClaw became popular. Hundreds of thousands of people, perhaps millions if you include the people who were standing in line in China to get access to an open claw, really got their hands dirty figuring out the guts of how these agents work. And while you don't necessarily see everyone running their Mac Mini setups anymore, the explosive learning of that early period of openclaw I think will be seen as a key inflection point. Moment for the history of agentic AI. Now, of course, all of this wasn't just happening to individual builders, and the evidence that something fundamental shifted started showing up, particularly on the revenue side of the ledger for the big labs. For the first few months of this year, it seemed like every time we turned around, Anthropic in particular had released some new jaw dropping number about how much their revenue run rate had grown, eventually eclipsing OpenAI, although it's not like they've been particularly slow in their revenue growth either. Now, the interesting thing is that the enterprise experiences the inverse side of that revenue chart as a cost chart, and on the one hand this was always inevitable. For years we've been talking about the idea that AI in the enterprise is not just another category of software spend, but represented something fundamentally different, something more akin perhaps to labor. The explosion of intelligence consumption reflected in that growing revenue and the growing cost for enterprises were simply a manifestation of that fact coming to bear. Now, as an aside, the recognition that we were not talking about seats, but instead talking about tokens did a whole lot to collapse the AI bubble narratives on Wall street from Q4 of last year as well. Pretty soon we were getting stories of enterprises absolutely torching their annual budgets in just a few short months. Uber was the most notable of this, and although these stories were presented as surprising, if you actually think about it, it's really not that surprising at all. How are we going to expect organizations to effectively budget for the agentic token era of AI when no one knew that that was right around the corner when those budgets were being made subsequently? And regular listeners of this show will know that these are the themes that have dominated for the past several months. We have seen adaptation to this new agentic paradigm run in all sorts of different directions. In some corners we're seeing token caps where companies are going with limits per user per month. We're studying companies have to experiment with and try to figure out measurement and monitoring and observability systems as cost spiral. It puts a whole new emphasis, something that was already coming up in the harness conversation around the fact that we were no longer just talking about AI as a choice of which models, but as an architectures and systems design question. The router, of course the product du jour is one response to this. But when it comes to enterprise buyers and planners and strategists, I don't think anyone and certainly my conversations this week at the KPMG event have confirmed this is looking to open router or any other solution as some silver bullet that's going to solve all these problems. And there are new problems. Specifically, the capability gap is growing on both an individual and an organizational level. The capability gap, of course, is the space between what AI can do and the value that we're getting out of it. Now, the good news is that it's grown largely because the upper bound of what AI can do is rocketing upwards at an incredible rate. And yet still there are real consequences to that gap widening. One of my bully pulpit issues is that I believe that the upskilling bill is coming due in a huge way when AI learning was just about whether you could prompt, well, maybe you could get away with not investing a ton in training your workforce. Now, on the other hand, we are talking about a fundamentally new work primitive. The way that people work is changing in a core way in many disciplines and functions from I do my work to I manage agents that do my work for me. The need that that creates for training is radically heightened from the previous era of AI. And indeed, one of the things that a lot of folks are talking about here at this event is how to deal with apportioning these incredibly powerful tools that are inherently technical tools to folks that aren't engineers and aren't technical by background. There are a lot of stories floating around this event of people accidentally unleashing agents on critical systems not because even necessarily they were doing anything wrong they, but because there weren't the right guardrails or access provisioning and these incredibly capable models with their new tenacity just didn't stay in their boxes. Now, this is not an upskilling question alone. Again, the watchword at the moment is systems and architectures, but without that training organizations are almost doomed to face this sort of issue in increasing fashion, or on the other hand, restrict the opportunity for people who could really be doing incredibly valuable work with these tools to do so because they're not trusted to do so. And this gets us to the questions that were explored not only in the panel discussion that followed this presentation, but honestly in the side conversations all over the event as well. The first question is how are enterprises redesigning for the agentic era? And the key word here is redesigning. The biggest caution that folks like Steve Chase from KPMG on the panel had was the warning of the problems with and ill effects of trying to simply bolt on an AI strategy to existing processes and systems. Now that has always been problematic and at least under maximizing for the potential of AI, even when we were firmly in the assisted AI and efficiency AI era. But in this time of new agenda capability, that gets even worse. Relatedly, the second question is about the nature of that redesign and why organizations need to be thinking in terms of architectures, systems, not just models. If previously an organization's response to some new challenge brought by technology was to figure out which vendor was best suited to solving that problem, that is simply insufficient for the moment that we find ourselves in now. Thinking about architectures means thinking about complex model systems that allow different levels of intelligence for different types of tasks. It means thinking about, yes, the routing systems, whether they are products off the shelf or bespoke or something else that allow that routing to happen. But it's also about that harness design, about which functions and people have access to what types of context and data and systems integration and what the guardrails that surround it need to be. And as we get into the third question, how are you provisioning costs across different groups? The big thing that underlies that is another systems design need, which is systems for monitoring and measuring AI usage. You have not seen the word token used more at an event since the height of the crypto era, man, and obviously the tokens we're talking about at this event are very different. But there is a very broad recognition here that without better visibility into the cost of AI and its relationship with outputs, it gets very hard to figure out which individuals, which groups, which functions, which projects should be getting access to which types of models and at what magnitude. Given that bully pulpit I mentioned before, I have certainly been gratified to see how big a concern enablement in education really is among these organizations. If I had to characterize the average discourse I've seen around that, there is a lot of throwing up of the hands and saying screw it, we're just going to have to do this ourselves and experimentation with bespoke customized solutions for this that work for the organization and the population that it has. In other words, there's a recognition that this is not going to be a bunch of cute video courses of the pattern of corporate trainings your but instead is going to involve real messy work of getting people to use these tools in new ways to do new things and then figure out how to transmit knowledge between parts of the organization that are figuring it out well versus parts that are not figuring it out so well. Indeed, one of the big patterns that I am seeing over and over and over again is various forms of collaboration between both AI redesigned software engineering organizations and business units, but also AI early adopters and AI champions and other types of business units. I Think the sophistication in the conversation is that no one is talking about the marketing folks replacing the engineers, but they are now Talking about the 10 or 20% of the types of skills, and even more than that, mindsets that engineers or product managers have that can become a part of the essential toolkit for those people in other functions, be it marketing or sales or back office or what have you, and how to best do that new sort of transmission. Now, I would say that a lot of the discourse at this event has been focused on internal transformation. 2026 is very clearly the year that for this representative sample of enterprises, AI is not a technology problem, but a transformation problem has really come home to roost as the reality. And yet there is also the entire dimension of agentic transformation that has to do with what happens externally as well. In other words, how are agentic opportunities reshaping business cases? Some of the examples of that that people are discussing here include shifts to the business model, people experimenting with outcomes based pricing instead of input based pricing like hourly billing. There is some discussion of new types of products and new types of services that become available in this new context. And there's also a lot of reevaluation of what the core state of the old product actually means. What is, for example, an audit. If agents can be doing a lot of that work, and if they can be doing it not just on a one off basis, but on a persistent basis, it feels to me as though that while that type of conversation is happening, most organizations are viewing themselves as patient zero, let's call it, for whatever their external AI strategy is, and are focusing on shoring up how they work first before necessarily making radical changes to what they sell externally. Although certainly for certain types of organizations, that change is being forced upon them. Now, of course, when it comes to business model disruption, it's made all the more difficult by the fact that no one gets to just shut things down for six months to figure this all out. They gotta do it in real time, even as they're servicing legacy customers on legacy products with legacy methods of delivery. And on top of all of this, the last question that we explored and that was floating around here is if and as we are successful in designing new systems, how can we build dynamism into that that has almost planned obsolescence and an appreciation of ephemerality built into it? The harnesses around them are going to change, interaction patterns are going to change, customer expectations are going to change, market expectations are going to change, policy is going to change. And so whatever new that gets built has to assume and design for the fact that a few months down the line from whenever it is ready will likely require it to change all over again. If all of this sounds head spinning, it is. But I think that there is something immensely positive. Last year, even at this event, which is about as AI pilled as an enterprise event can be, there were still, as I said, so many IF questions. How do I convince others in my organization that this is real and that we should be doing it? How do I show ROI to prove that what we're doing is worth the time and money that we're spending on it now? It's not that ROI questions and things of the like are gone, but by and large the questions that people are asking now are, it feels like to me, the foundational questions for redesigning for a new era that we are going to be answering for the next, call it half decades. Companies asking about designing and allocating token budgets are now exploring this new category of spend that is just going to become an essential part of their organization. When companies are talking about building observability systems around the new intelligence they're using, while the models and harnesses may change, it is very likely that whatever gets updated is still going to need that sort of observability. I guess the point is that the paradigm shift has happened for years. Basically, since the ChatGPT moment, enterprises have been anticipating the shift from assisted AI to agentic AI, the opportunity for AI not just to help us do work, but to actually do the work itself. Now that that is here, all of the questions are about how we solve all the new problems that that new way of working brings and how we best seize the opportunities that it opens up. Almost none of the questions have answers right now, but it should feel good. I think that the questions being asked are the right ones. Anyways, thanks to KPMG for having me out. It was a great event and I look forward to coming back next year where honestly, I can't even imagine how different it's going to be by then. For now, that's going to do it for today's AI Daily Brief. Appreciate you listening or watching as always and until next time, peace.

Summary6 min read

The AI Daily Brief: Artificial Intelligence News and Analysis

Episode: 6 Questions Every Enterprise Has to Answer About AI
Host: Nathaniel Whittemore (NLW)
Date: July 30, 2026

Overview of the Episode

In this episode, Nathaniel Whittemore focuses on the rapidly evolving landscape of enterprise AI, highlighting six critical questions companies must address to harness agentic AI effectively. Drawing from his recent presentation at KPMG’s Tech and Innovation Symposium, NLW delivers a deep dive into how the paradigm has shifted—from “if” enterprises should use AI to “how” they can strategically redesign their organizations for this new era.

The episode is divided into two main sections:

A news roundup on key developments in AI policy, industry competition, and public discourse.
An in-depth exploration of six fundamental enterprise AI questions, framed by recent research and his observations at the KPMG event.

Key Discussion Points and Insights

1. Current Headlines in AI (00:30 - 18:40)

Sam Altman in Washington:
Sam Altman, CEO of OpenAI, visits lawmakers to discuss the latest model preview, amidst significant complications due to recent hacks and debates over open vs. closed model access.
- "Altman declined to state when or even whether the model being previewed would be released, commenting, 'Not sure that's the part we're here to talk about.'" (02:55)
- Altman’s focus was more on competitiveness and government involvement in AI safety than on specific model releases.
Model Security and Public Policy:
The Hugging Face hack and the “open weights” debate prompt broader government discussions about regulating AI development speed.
OpenAI’s Financials and Hardware Ambitions:
OpenAI is seeing unprecedented revenue growth—now eclipsed by Anthropic—and is working on a “family of devices” to give chatbots physical presence.
- "President Greg Brockman says that the company is working on an entire range of devices to give a physical presence to their chatbots." (06:40)
Microsoft and Enterprise Competition:
Microsoft is prepping a “Copilot super app” to unify AI experiences and position itself as a model-agnostic platform. Focus is on giving enterprises control and flexibility.
- “We are very, very clear about the architectural design of the platform, which is you get to keep your harness separate from the model... That means any model at any given time is swappable.” – Satya Nadella (08:50)
Mark Zuckerberg's Op-Ed on AI Optimism:
Zuckerberg pushes for AI acceleration and widespread access, arguing that the risk is more about concentration of AI power than the technology itself.
- “It is surprising that the discourse from many of those who are developing artificial intelligence is so filled with doom... Rather than centralizing this power, we believe that delivering personal superintelligence to everyone is the way to answer this question.” – Mark Zuckerberg (11:40, 13:25)

2. Enterprise AI: The Six Defining Questions (18:40 - End)

Retrospective on Enterprise AI Evolution (18:40 - 27:20)

In 2025, mainstream enterprise AI conversations were still exploring “if” questions; now, the focus is on “how.”
Inflection point: Major model upgrades (Opus 4.5, GPT-5.2) at the end of 2025 shifted agentic AI from the future to immediate reality.
Accelerated adoption as organizations moved from coding to managing agents that code or execute work.

The Cost and Consumption Paradigm Shift

AI is no longer just another software category—it's closer to labor: enterprises now have to manage “intelligence consumption.”
Exploding costs and token usage require new budgeting, monitoring, and optimization techniques.
- “You have not seen the word token used more at an event since the height of the crypto era, man, and obviously the tokens we're talking about at this event are very different.” (40:20)

Upskilling and Talent Transformation

The capability gap is widening: it's not about using AI, but using it well.
Upskilling now focuses on teaching people to manage agentic workflows rather than just prompt engineering.
- “...We're talking about a fundamentally new work primitive. The way that people work is changing in a core way in many disciplines and functions from ‘I do my work’ to ‘I manage agents that do my work for me.’” (35:50)

The Six Crucial Enterprise AI Questions (40:00 - End)

1. How are Enterprises Redesigning for the Agentic Era?

Caution against “bolting on” AI to existing processes; demands a full rethink of systems and workflows.
- “The biggest caution...was the warning of the problems with and ill effects of trying to simply bolt on an AI strategy to existing processes and systems.” (42:20)

2. Are You Designing Architectures and Systems, Not Just Picking Models?

Response to challenges must move beyond vendor selection to considering complex ecosystems—routing systems, data access, guardrails, and dynamic harnesses.
- “...It’s also about...which functions and people have access to what types of context and data and systems integration and what the guardrails that surround it need to be.” (43:40)

3. How Are You Provisioning and Budgeting AI Costs?

With token-based pricing and skyrocketing consumption, enterprises need measurement, monitoring, and cost attribution across teams and functions.
- “...Without better visibility into the cost of AI and its relationship with outputs, it gets very hard to figure out which individuals, which groups, which functions, which projects should be getting access to which types of models and at what magnitude.” (44:40)

4. How Are You Enabling and Educating Your Workforce?

Effective AI use requires new mindsets and real, ongoing experimentation—not generic video courses.
Emphasis on peer learning and knowledge transmission within organizations.
- “There is a recognition that this is not going to be a bunch of cute video courses...but instead is going to involve real messy work of getting people to use these tools in new ways...” (45:30)

5. How Are You Navigating External, Business Model Disruption?

Agentic AI creates new opportunities for product and business model transformation (e.g., outcome-based pricing, persistent auditing).
Most are prioritizing internal transformation before overhauling external offerings, but disruption is happening in some sectors already.
- “...Most organizations are viewing themselves as patient zero...for whatever their external AI strategy is.” (47:40)

6. How Will You Design for Dynamism and Ephemerality?

Systems must anticipate rapid, ongoing change: “planned obsolescence” should be part of the new design.
- “Whatever new that gets built has to assume and design for the fact that a few months down the line...will likely require it to change all over again.” (49:00)

Notable Quotes & Memorable Moments

On mindset shift:
“2026 is very clearly the year that for this representative sample of enterprises, AI is not a technology problem, but a transformation problem has really come home to roost as the reality.” (46:50)
On measuring value:
“The capability gap...is the space between what AI can do and the value that we're getting out of it. The good news is that it's grown largely because the upper bound of what AI can do is rocketing upwards at an incredible rate.” (34:20)
On optimism in the face of change:
“The paradigm shift has happened...all of the questions are about how we solve all the new problems that that new way of working brings and how we best seize the opportunities that it opens up.” (54:00)

Timestamps for Key Segments

| Segment | Timestamps | |-----------------------------------------------|----------------------| | Major AI Policy & Industry News | 00:30 – 18:40 | | Enterprise AI Retrospective & Paradigm Shift | 18:40 – 27:20 | | Growth in AI Costs and Capability Gap | 27:20 – 35:50 | | Upskilling and Agent Management | 35:50 – 40:00 | | The Six Enterprise AI Questions | 40:00 – 53:00 | | Designing for Dynamism/Ephemerality | 49:00 – 53:00 | | Final Reflections and Closing | 53:00 – end |

Tone and Takeaways

The tone is conversational yet analytical, frequently referencing “friends” and using accessible metaphors (“patient zero,” “collapse the AI bubble,” etc.).
NLW stresses optimism and urgency: the old “if” questions are gone, replaced by deeper questions about “how” to transform.
Takeaway: Enterprises must move beyond adoption toward systemic redesign, embracing architectural thinking, new cost models, continuous upskilling, and built-in adaptability to remain competitive in the agentic AI era.

This summary provides a roadmap for enterprises navigating AI transformation—and for listeners seeking to catch up on the most critical themes shaping AI deployment in real organizations today.

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Today on the AI Daily Brief, six questions Shaping Enterprise AI before that in the headlines, Sam Altman goes to Washington and the conversation has gotten a lot more complicated over the last week. 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 Blitzy, Retool and Airtable. To get an ad free version of the show, go to patreon.com aidaily brief or you can subscribe on Apple Podcasts. To learn more about sponsoring the show, send us a Note@ SponsorsIDailyBrief AI and a bone up on your AI skills with the last month or so of summer, go check out our latest free self directed education program, this one being a choose your own summer adventure. You can find it at summeradventure AI well, Sam Altman has arrived in Washington to meet with lawmakers and White House officials. And when the trip was set at the beginning of last week, the agenda was pretty simple. Altman would brief Washington on the capabilities of OpenAI's new model and discuss a protocol for release, hopefully avoiding a repeat of the Fable and GPT5.6 rollout. Since then, however, we've had the OpenAI hugging face hack, a public debate about Open Weight's models, and an attention getting petition for the government to step in and build the capability to slow down the pace of Frontier AI. In other words, conversations have become a lot more complicated for Altman in just a couple of weeks. According to reports, Altman met with Senate Commerce Chair Ted Cruz and several Democrat senators on Wednesday, but we got very little information on what was actually discussed. Speaking to reporters, Altman declined to state when or even whether the model being previewed would be released, commenting, not sure that's the part we're here to talk about. Altman also declined to discuss the new capabilities of the model that give cause for concern. Now, of course, the hugging face incident looms large over this visit, but it increasingly appears like the model at the center of that controversy will not see release. In a Tuesday update to their postmortem blog, OpenAI said that the model was an internal only research prototype never intended for public release. In Washington, Altman told the press that the model has now been permanently deactivated and is inaccessible even for internal research, meaning presumably it's not the model being previewed to lawmakers this week. Now, Sam said that he and Ted Cruz had not discussed specific legislation, but that quote, we talked about our new model and what it's going to take for America to remain competitive with AI. Altman also said that he didn't support mandatory safety testing, particularly because it could introduce an unnecessary burden on Open Weight's model developers, but added for frontier models at new levels of capabilities, we think it's really important that the federal government has great testing capacity and capabilities. Altman said he plans to meet with a range of other officials to end the week, including White House Chief of Staff Susie Wiles, who for whatever reason has wound up as one of the key decision makers on AI policy. That meeting will likely include a discussion of the voluntary AI safety testing framework, which has a deadline of August 1. Reports state that this framework has been circulated to OpenAI, Anthropic and Google for comment, but Altman declined to comment on the draft. In a hallway interview on Capitol Hill, Altman was asked whether he would talk to the White House about the need to decelerate AI development. Representing the views of his staff from the recent open letter, Altman responded, I wouldn't use the word deceleration, but we talk about the need to pace it as the models get more capable, which I think is in everyone's interests. Now. One other story that I'm going to get into in more depth tomorrow is is the significant increase in revenue numbers on both the OpenAI and Anthropic front. But I do not want to bury that in the headlines. So that will be a major topic for tomorrow. Come back for that. Suffice it to say The CFO of OpenAI, Sarah Fryer, recently told employees that annualized revenue in July topped all of the previous quarter. One more bit of OpenAI intrigue President Greg Brockman says that the company is working on an entire range of devices to give a physical presence to their chatbots. In a new interview with former Wall Street Journal reporter Joanna Stern, Brockman confirmed that OpenAI's hardware plans are still on track, stating that the company is building a family of devices. He wouldn't confirm the recently rumored smart speaker or any other form factors that have seen speculation this year, nor would he give a timeline beyond commenting. You can expect them soon. Still, this is the clearest confirmation we've had so far that a full hardware range is still on the roadmap, surviving the end of SideQuest and an IP lawsuit from Apple. Brockman was understandably brief when talking about that lawsuit, stating, we are focused on our own development and technology. One interesting bit of competitive news, which I think sounds good for consumers, particularly those of you who are in the enterprise without a ton of choice on which models and platforms you're going to use. Microsoft appears to be gearing up to compete more directly with OpenAI and anthropic with the development of a Copilot super app. During Wednesday night's earnings call, CEO Satya Nadella confirmed the app is coming later this year with the goal of unifying the Copilot experience for both consumer and enterprise customers. He said Copilot is rapidly evolving from chat to cowork to autopilot. This quarter we are bringing these Copilot experiences together, including code in one super app. This is a major step forward and I look forward to sharing more soon. Microsoft is beginning to see OpenAI and Anthropic as direct rivals thanks to the capabilities of their new MAI models. Nadella told analysts that the combination of cost and data privacy concerns gives Microsoft an opportunity to sell customers on their own, cheaper models. When asked about the rolling debate about open versus Closed, Nadella suggested the framing is too simplified. He said the goal is to have the firm be in control of their own destiny. We are very, very clear about the architectural design of the platform, which is you get to keep your harness separate from the model. That means any model at any given time is swappable. Now, I'm sure some of you will think that that's kind of a corporate answer, but I actually think that his assessment of how most enterprises feel is correct in that I don't think that most enterprises actually care ultimately about whether a model is open or closed. They care what they can do with it, what control they have, and what sacrifices around control they're making to someone else to have access to the systems they're using anyway. Overall, Microsoft is increasingly positioning themselves not as a reseller of OpenAI or anthropic products, but rather as a model agnostic platform offering a full range of options, said Nadella. Every customer wants the right model for each task based on latency, quality, cost and compliance. We offer the broadest model catalog in the cloud with over 11,000 models, including the leads from OpenAI, Anthropic, Mistral, Xai as well as our own MAI family. Now, along the swirl of all these big discussions and jockeying for position in AI, Mark Zuckerberg has made the case for AI acceleration in a new op ed in the Wall Street Journal. In an essay titled the AI Future is for Everyone, Zuckerberg argued that the defining question of the AI age won't be whether superintelligence will exist, but who will have access to it. In other words, whether we end up in a world where superintelligence is closely held by a handful of institutions or broadly distributed to normal people, zuckerberg wrote. It is surprising that the discourse from many of those who are developing artificial intelligence is so filled with doom. I don't understand why anyone who believes that AI will eliminate most jobs and much of humanity's relevance would rush to build that future. This is, for what it's worth, exactly the point that I was trying to make yesterday when I was discussing what I think the normie response to the pacing the Frontier letter would be that the only acceptable answer to why are you building AI? Is not well, if we don't, someone else will, but but instead because we think AI will be awesome and dramatically better than all the risks that it comes with, zuckerberg continued. The notion that AI is so dangerous that the only safe path is an extreme concentration of power seems dangerous historically, hoping that an absolute power will benevolently provide for humanity if sufficiently enlightened hasn't led to safe or positive outcomes. Zuckerberg's view is that much like previous technologies like the Internet, the best result will come from diffusing the technology freely across society, he wrote. Rather than centralizing this power, we believe that delivering personal superintelligence to everyone is the way to answer this question. This has the potential to begin a new era of personal empowerment in which individuals have greater freedom to pursue their interests and reach their full potential now. The op ed came as part of a press tour that linked up with Meta's new AI optimism campaign. In a separate interview with the Journal, Zuckerberg called for the US government to accelerate AI development rather than restrict it. He argued that the benefits of broadly distributing AI outweigh the risks by quite a margin, adding, I get that it's always hard to debate about the future because it hasn't happened yet, but I do think we have a lot of data points at this point, and that should point us to be much more optimistic than I believe the current discourse reflects. Specifically, he warned against thinking a 30 or 60 day government review window is harmless, commenting, the field is moving so quickly, that actually is quite a meaningful amount of time now. Notably, Meta is the only frontier AI lab that hasn't agreed to the government's voluntary testing framework. Zuckerberg also said that the US government shouldn't ban Chinese AI in a separate interview with the Financial Times. Not only does he think a ban won't be effective, but he believes it would open the risk of regulatory capture and could stymie the release of open models more generally now met as AI CEO Alexander Wang recently said that the company will begin launching open source models again, suggesting that this isn't just hollow sentiment. Still, overall, the core message is simply that more AI optimism is needed. Speaking with the New York Times, Zuckerberg said, so much of the discourse from a lot of the other labs that are developing this is overwhelmingly filled with doom. There needs to be a voice or several voices that are bringing realism to this debate. Now. I think. Unfortunately, Mark Zuckerberg's power to be the leading face of AI optimism is limited by history and people's fairly negative view of the overall impact of social media on society. Still, to start to have loud, sustained discourse that other people can pick up and run with is immensely important. And you better believe I will be here amplifying that message. For now, however, that's going to do it for today's headlines. Next up, the main episode. One of the most important AI questions right now isn't who's using AI? It's who's using it? Well, KPMG and the University of Texas at Austin just analyzed 1.4 million real workplace AI interactions and found something surprising. The highest impact Users aren't better prompt engineers. They treat AI like a reasoning partner. They frame problems, guide thinking, iterate, and push for better answers. And the good news? These behaviors are teachable at scale. If you're trying to move from AI access to real capability, KPMG's research on sophisticated AI collaboration is worth your time. Learn more at kpmg.com us sophisticated that's kpmg.com us sophisticated Blitzy's deep code based understanding unlocks the thing every roadmap owner cares about shipping new features. Here's the truth about building inside a massive enterprise code base. Writing code was never the bottleneck. Context is which system does this touch? Which contracts can't break? Which standards apply? Blitzi already knows because it reverse engineered your entire codebase into a dynamic knowledge graph before feature work began. With that complete picture, Blitzy builds features end to end architecture, APIs, UI and tests, all validated against your existing systems. One blitzy customer built an AI native application from scratch with 100% autonomous completion, saving over 2,700 engineering hours. Features that respect your code base instead of fighting it. Stop letting your backlog grow faster than your team. Accelerate your roadmap@blitzi.com that's B L I T Z D Y.com this episode is supported by Retool. AI made building software easier than ever, so more people are building it than ever, usually without a thought for security. Right now, people in your company are vibe coding, and every ungoverned app that touches your data is a risk you own. Retool takes that risk off your shoulders. Build apps however you want, natively with Retool or with Claude Code, Codex or any coding agent, and ship it in a secure, governed environment. Security lives in the platform, not in each app, so however it was built, it's governed the moment it ships. It's why teams at Amazon, Stripe and Brex build on Retool, and new enterprise customers who Sign up by September 30th get up to $10,000 in AI credits per year. Learn more at retool.com aidaily 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@hyperagent.com AIDAILY Brief welcome back to the AI Daily Brief. This week I had the chance to be out in Utah with KPMG for their annual Tech and Innovation symposium. Now this is the second year that I've been at the event and in each case have had a chance to do a similar type of presentation. This time around, both my focus and the focus of conversation after was about trying to sum up, broadly speaking, the big questions that are currently shaping how enterprises have to think about AI. And what's extremely notable to me is how much the conversation has changed since last year at this time. Now I'm going to go through a version of the presentation I gave, but before that I actually want to zoom back to last year. Last year I did, where AI is 15 slides and 15 minutes, which was actually, as you can see, 23 slides and looking back, it's almost quaint. What we found interesting or fascinating and what we were discussing at that event. The first theme was acceleration, and we talked about how AI wasn't just moving faster, but was actually getting faster in the speed it was being adopted. I discussed the more than 100% growth in the total monthly tokens that Google was processing between May and July, where they reached nearly a quadrillion tokens. Now, as many of you know, a quadrillion tokens at this point is about what a single open claw left unattended will do in a month. But it was a big deal back then to see this massive inflection point, and indeed some of the themes from that presentation were effectively setups to where we are now. The compute shortage has done nothing but get worse as we've moved to a new era. And of course, even back then the big conversation was agents. Now, what was interesting is that, at least in the way that we use agents today, agentic AI was still firmly in the domain of the future. This was The Claude Force Sonet03 type time horizon, and we were just wrapping our heads around the meter time horizon task graph that showed that AI capability was doubling every few few months. Now, speaking of themes that would continue to be important even back then, it was clear that agentic coding was the breakout agentic use case. Then again, to give a sense of just how long ago this was, we were all gobsmacked because we had hit a billion dollar revenue run rate in just about a single year. To put a fine point on how much this has changed, right before this I read a post from Dwarkesh that suggested that Anthropic could get to 100 or 150 billion revenue run rate this year. Now I won't go through all of these different slides, but what stands out to me reflecting back then was that the questions of AI and agents were really still for some if questions. One slide that I didn't have in this particular chart, but I know I had in a longer presentation that was being given around the same time, was this chart from McKinsey that showed the growth in the number of organizations that had implemented at least one or two or three different AI use cases. Yes, the big deal in mid-2025 was still that something like 40% of enterprises were up to two or three use cases a year on. The conversation has changed immensely and the historian in me thinks it's worth reflecting how we got here. The big capability jump, as we now know, came towards the end of the year, the November December time period where we got Opus 4.5 and GPT 5.2. For whatever set of reasons, those were the model updates where agents and agentic workflows actually came online in a major way. Now what was fascinating is that it actually took a couple of months for people to really grok that something had shifted. Everyone went home for the holiday, had a little bit of time to decompress, and when they fired up their instance of Claude code or whatever tool they were using, they found that the stuff that they could do was significantly different than what it had been before. I still remember vividly the absolute tidal wave of tweets in that week between Christmas and New Year's, of entrepreneur after entrepreneur and developer after developer coming back gobsmacked about what they could now build that they simply couldn't before. Now, what's interesting is that this almost immediately translated into organizational practice as well. Part of this was because software organizations had been adapting to greater and greater capabilities throughout the year and even before. By the turn of 2026, we were long past software engineering organizations viewing AI coding as just an autocomplete solution. And they became some of the first groups in the enterprise to actually shift from viewing their job as writing code to managing the agents that wrote the code for them. That said, maybe because the enterprise folks had been paying attention for over two years, at that point, it wasn't like there was some major lag from the AI early adopters to enterprises thinking about what this new agenda capacity was going to mean for their work and coming back into 2026. It was absolutely not just software engineering organizations that were racing to put into practice these new ways of working. You saw vanguard builders and early adopters across domains from marketing to legal to finance, starting to figure out how to bring this new capabilities into their work as well. Alongside the model, jump folks also recognized that part of the new capability set was actually about the harness that you situated the models. Now, Claude code had been growing in adoption throughout 2025, but became a real focal point in the new year, which was perhaps augmented by OpenAI going all in on their Codex product as well. Still, I think in many ways where this whole idea of harnesses and frankly a much deepened understanding of what we actually mean when we say agents and what it means to build and manage an agent, came when OpenClaw became popular. Hundreds of thousands of people, perhaps millions if you include the people who were standing in line in China to get access to an open claw, really got their hands dirty figuring out the guts of how these agents work. And while you don't necessarily see everyone running their Mac Mini setups anymore, the explosive learning of that early period of openclaw I think will be seen as a key inflection point. Moment for the history of agentic AI. Now, of course, all of this wasn't just happening to individual builders, and the evidence that something fundamental shifted started showing up, particularly on the revenue side of the ledger for the big labs. For the first few months of this year, it seemed like every time we turned around, Anthropic in particular had released some new jaw dropping number about how much their revenue run rate had grown, eventually eclipsing OpenAI, although it's not like they've been particularly slow in their revenue growth either. Now, the interesting thing is that the enterprise experiences the inverse side of that revenue chart as a cost chart, and on the one hand this was always inevitable. For years we've been talking about the idea that AI in the enterprise is not just another category of software spend, but represented something fundamentally different, something more akin perhaps to labor. The explosion of intelligence consumption reflected in that growing revenue and the growing cost for enterprises were simply a manifestation of that fact coming to bear. Now, as an aside, the recognition that we were not talking about seats, but instead talking about tokens did a whole lot to collapse the AI bubble narratives on Wall street from Q4 of last year as well. Pretty soon we were getting stories of enterprises absolutely torching their annual budgets in just a few short months. Uber was the most notable of this, and although these stories were presented as surprising, if you actually think about it, it's really not that surprising at all. How are we going to expect organizations to effectively budget for the agentic token era of AI when no one knew that that was right around the corner when those budgets were being made subsequently? And regular listeners of this show will know that these are the themes that have dominated for the past several months. We have seen adaptation to this new agentic paradigm run in all sorts of different directions. In some corners we're seeing token caps where companies are going with limits per user per month. We're studying companies have to experiment with and try to figure out measurement and monitoring and observability systems as cost spiral. It puts a whole new emphasis, something that was already coming up in the harness conversation around the fact that we were no longer just talking about AI as a choice of which models, but as an architectures and systems design question. The router, of course the product du jour is one response to this. But when it comes to enterprise buyers and planners and strategists, I don't think anyone and certainly my conversations this week at the KPMG event have confirmed this is looking to open router or any other solution as some silver bullet that's going to solve all these problems. And there are new problems. Specifically, the capability gap is growing on both an individual and an organizational level. The capability gap, of course, is the space between what AI can do and the value that we're getting out of it. Now, the good news is that it's grown largely because the upper bound of what AI can do is rocketing upwards at an incredible rate. And yet still there are real consequences to that gap widening. One of my bully pulpit issues is that I believe that the upskilling bill is coming due in a huge way when AI learning was just about whether you could prompt, well, maybe you could get away with not investing a ton in training your workforce. Now, on the other hand, we are talking about a fundamentally new work primitive. The way that people work is changing in a core way in many disciplines and functions from I do my work to I manage agents that do my work for me. The need that that creates for training is radically heightened from the previous era of AI. And indeed, one of the things that a lot of folks are talking about here at this event is how to deal with apportioning these incredibly powerful tools that are inherently technical tools to folks that aren't engineers and aren't technical by background. There are a lot of stories floating around this event of people accidentally unleashing agents on critical systems not because even necessarily they were doing anything wrong they, but because there weren't the right guardrails or access provisioning and these incredibly capable models with their new tenacity just didn't stay in their boxes. Now, this is not an upskilling question alone. Again, the watchword at the moment is systems and architectures, but without that training organizations are almost doomed to face this sort of issue in increasing fashion, or on the other hand, restrict the opportunity for people who could really be doing incredibly valuable work with these tools to do so because they're not trusted to do so. And this gets us to the questions that were explored not only in the panel discussion that followed this presentation, but honestly in the side conversations all over the event as well. The first question is how are enterprises redesigning for the agentic era? And the key word here is redesigning. The biggest caution that folks like Steve Chase from KPMG on the panel had was the warning of the problems with and ill effects of trying to simply bolt on an AI strategy to existing processes and systems. Now that has always been problematic and at least under maximizing for the potential of AI, even when we were firmly in the assisted AI and efficiency AI era. But in this time of new agenda capability, that gets even worse. Relatedly, the second question is about the nature of that redesign and why organizations need to be thinking in terms of architectures, systems, not just models. If previously an organization's response to some new challenge brought by technology was to figure out which vendor was best suited to solving that problem, that is simply insufficient for the moment that we find ourselves in now. Thinking about architectures means thinking about complex model systems that allow different levels of intelligence for different types of tasks. It means thinking about, yes, the routing systems, whether they are products off the shelf or bespoke or something else that allow that routing to happen. But it's also about that harness design, about which functions and people have access to what types of context and data and systems integration and what the guardrails that surround it need to be. And as we get into the third question, how are you provisioning costs across different groups? The big thing that underlies that is another systems design need, which is systems for monitoring and measuring AI usage. You have not seen the word token used more at an event since the height of the crypto era, man, and obviously the tokens we're talking about at this event are very different. But there is a very broad recognition here that without better visibility into the cost of AI and its relationship with outputs, it gets very hard to figure out which individuals, which groups, which functions, which projects should be getting access to which types of models and at what magnitude. Given that bully pulpit I mentioned before, I have certainly been gratified to see how big a concern enablement in education really is among these organizations. If I had to characterize the average discourse I've seen around that, there is a lot of throwing up of the hands and saying screw it, we're just going to have to do this ourselves and experimentation with bespoke customized solutions for this that work for the organization and the population that it has. In other words, there's a recognition that this is not going to be a bunch of cute video courses of the pattern of corporate trainings your but instead is going to involve real messy work of getting people to use these tools in new ways to do new things and then figure out how to transmit knowledge between parts of the organization that are figuring it out well versus parts that are not figuring it out so well. Indeed, one of the big patterns that I am seeing over and over and over again is various forms of collaboration between both AI redesigned software engineering organizations and business units, but also AI early adopters and AI champions and other types of business units. I Think the sophistication in the conversation is that no one is talking about the marketing folks replacing the engineers, but they are now Talking about the 10 or 20% of the types of skills, and even more than that, mindsets that engineers or product managers have that can become a part of the essential toolkit for those people in other functions, be it marketing or sales or back office or what have you, and how to best do that new sort of transmission. Now, I would say that a lot of the discourse at this event has been focused on internal transformation. 2026 is very clearly the year that for this representative sample of enterprises, AI is not a technology problem, but a transformation problem has really come home to roost as the reality. And yet there is also the entire dimension of agentic transformation that has to do with what happens externally as well. In other words, how are agentic opportunities reshaping business cases? Some of the examples of that that people are discussing here include shifts to the business model, people experimenting with outcomes based pricing instead of input based pricing like hourly billing. There is some discussion of new types of products and new types of services that become available in this new context. And there's also a lot of reevaluation of what the core state of the old product actually means. What is, for example, an audit. If agents can be doing a lot of that work, and if they can be doing it not just on a one off basis, but on a persistent basis, it feels to me as though that while that type of conversation is happening, most organizations are viewing themselves as patient zero, let's call it, for whatever their external AI strategy is, and are focusing on shoring up how they work first before necessarily making radical changes to what they sell externally. Although certainly for certain types of organizations, that change is being forced upon them. Now, of course, when it comes to business model disruption, it's made all the more difficult by the fact that no one gets to just shut things down for six months to figure this all out. They gotta do it in real time, even as they're servicing legacy customers on legacy products with legacy methods of delivery. And on top of all of this, the last question that we explored and that was floating around here is if and as we are successful in designing new systems, how can we build dynamism into that that has almost planned obsolescence and an appreciation of ephemerality built into it? The harnesses around them are going to change, interaction patterns are going to change, customer expectations are going to change, market expectations are going to change, policy is going to change. And so whatever new that gets built has to assume and design for the fact that a few months down the line from whenever it is ready will likely require it to change all over again. If all of this sounds head spinning, it is. But I think that there is something immensely positive. Last year, even at this event, which is about as AI pilled as an enterprise event can be, there were still, as I said, so many IF questions. How do I convince others in my organization that this is real and that we should be doing it? How do I show ROI to prove that what we're doing is worth the time and money that we're spending on it now? It's not that ROI questions and things of the like are gone, but by and large the questions that people are asking now are, it feels like to me, the foundational questions for redesigning for a new era that we are going to be answering for the next, call it half decades. Companies asking about designing and allocating token budgets are now exploring this new category of spend that is just going to become an essential part of their organization. When companies are talking about building observability systems around the new intelligence they're using, while the models and harnesses may change, it is very likely that whatever gets updated is still going to need that sort of observability. I guess the point is that the paradigm shift has happened for years. Basically, since the ChatGPT moment, enterprises have been anticipating the shift from assisted AI to agentic AI, the opportunity for AI not just to help us do work, but to actually do the work itself. Now that that is here, all of the questions are about how we solve all the new problems that that new way of working brings and how we best seize the opportunities that it opens up. Almost none of the questions have answers right now, but it should feel good. I think that the questions being asked are the right ones. Anyways, thanks to KPMG for having me out. It was a great event and I look forward to coming back next year where honestly, I can't even imagine how different it's going to be by then. For now, that's going to do it for today's AI Daily Brief. Appreciate you listening or watching as always and until next time, peace.