The AI Daily Brief: Artificial Intelligence News and Analysis - What the Best Business AI Users Are Doing Different

Starting point is 00:00:00 The businesses that are using AI the best are really doing things a little bit differently. According to a recent survey, they are building model routers, building organizational and data sovereignty strategies, and generally making their AI management layer much more robust. Along with that, what they are using AI for and the value that they are seeing from it is changing, and in all of this, they are building a template that other businesses can follow, and that's what we'll be discussing today. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's

Starting point is 00:00:41 sponsors, KPMG, Section, Harbor, and Granola. To get an ad-free version of the show, go to Patreon.com slash AI Daily Brief, or you can subscribe on Apple Podcasts. And to learn more about sponsoring the show, send us a note at sponsors at AIdailybrief.aI. A lot of our discussion recently has been around the new emerging competition for personal AI agents, and an early win in those Agent Wars, has delivered Meta stock its best month in years. Meta stock was up 27% in September, even with the 5% slide this week on news that OpenAI was launching a competitor to Muse. That made it Meta's best single month performance since November of 2022, when the company began their year of efficiency with mass layoffs, hiring freezes, and a bit of temperance on

Starting point is 00:01:24 their Metaverse plans. Mew's early success has so far added half a trillion dollars in market cap. But even more important than that, it has given the market an indication that meta has a viable AI strategy. The Wall Street consensus has pushed meta to a strong buy, but not everyone is convinced. Needham analyst Laura Martin is one of the few sticking with a hold rating in a Thursday note. Giving them credit, she wrote that meta has, quote, clearly pivoted away from the Metaverse and towards personal agentic AI with Muse at the center. However, she's skeptical of the payoff, noting that Meta is, in her words, notoriously slow at monetizing new products. And indeed, Muse is currently free for all but the biggest power

Starting point is 00:02:03 users, and META has said they plan to keep user data segregated from their ad business. Even with the launch of an enterprise platform earlier this week, Morningstar argued that meta's ability to run an enterprise business is, quote, unproven, as consumer products remain at the center of the company. Still, if you're meta, you've got to be feeling pretty good. Six months ago, investors were questioning the company's basic competence in AI. So the fact that they're now questioning meta's ability to monetize their successful bets is a huge shift. Now, staying on the markets theme, Anthropic is pushing to get their IPO out before Thanksgiving, with investor meetings set for later this month.

Starting point is 00:02:39 Bloomberg reports that Anthropic aims to begin marketing the IPO in the week of November 9th. That would give them around two weeks for the roadshow, assuming the first day of trading somewhere early on the week of Thanksgiving. Sources said that the timeline is still subject to change, but Anthropic is pushing hard to get the deal out by the end of the year. Ahead of the marketing campaign, Anthropic is planning to host an investor day on October 14th. institutional investors who may participate in the IPO have been invited to the event, which will give them an opportunity to meet with senior management.

Starting point is 00:03:07 News that the timeline is firming up comes after Reuters leaked portions of Anthropics S1 prospectus earlier in the week. The S1 revealed that Anthropic had an operating loss of $8 billion on revenue of $4.6 billion in 2025. There has been a lot of doomsaying around these numbers, but my position, which I feel very, very strongly about, is that the idea that when push comes to shove, investors are going to care about 2025 numbers when Anthropic has more than 10xed growth in 2026, I just think is treating Anthropic like they're a company from the pre-AI days, which they are very decidedly not.

Starting point is 00:03:40 Now, that is not at all to say that investors are going to love everything they find in the complete prospectus, just that to the extent the IPO underperforms, it won't be their operating loss from 2025 that did it. And I appear to be not alone in this, with Bloomberg reporting that potential IPO investors believe the company can achieve its target valuation of between $1.8 and $2 trillion. This is, by the way, expected to be the largest IPO in history, taking in more than the $75 billion raised by SpaceX. Joking about that insanely high target valuation and the inevitable dip that comes after, Likidity posted,

Starting point is 00:04:11 If Anthropic goes public before Thanksgiving, I'd rather just wait to invest during their Black Friday slash Cyber Monday 25% discount. Meanwhile, staying on Anthropic, perhaps against many expectations, President Trump seems to have taken a liking to Dario. On Thursday, Time magazine published a wide-ranging interview. with the president, and one of the quotes that stood out with Trump's positive impression on meeting the Anthropic CEO. I liked him and his wife a lot, Trump said. Very smart guy. Maybe different than I thought a little bit. Really a little bit different, but no, he understands. Trump met with Amade and his wife for a two-hour

Starting point is 00:04:43 dinner on Sunday ahead of this week's gathering of tech leaders. It seems the president came away with a more nuanced understanding of Dario's worldview. Trump commented, I spoke to him about his views, and they're much different, I think, than what is portrayed in the media. The comment suggests that at least some amount of the acrimony between the White House and Anthropic has been driven by staffers rather than Trump himself. In June, you might remember, Wired reported that staffers were relieved that Anthropic co-founder Tom Brown had taken over negotiations around Fable's release, because he was not, quote, being a weirdo like Dario. The Time article also unpacked just how much the president has gotten into using AI himself. A staffer said Trump had spent hours talking to Grock after a meeting

Starting point is 00:05:22 with Elon Musk in December, asking the chatbot about his presidential legacy. At the time, Trump was weighing up an escalation in Venezuela. He asked Grogh writes time, quote, how Venezuelans would react if the U.S. captured Maduro. Time continued. According to the official present, the chatbot responded that Maduro was a repressive and deeply unpopular dictator and that many Venezuelans would likely celebrate his downfall. After Trump ordered the mission to seize Maduro the following month, celebrations broke out in the streets.

Starting point is 00:05:48 Trump, according to officials, came away thinking Grock was ingenious. That led a lot of folks to jump to the next conclusion, summed up by Hacchio on X. Wait, so there's a chance the Iran fiat. is happening because some LLM told him that decapitation is going to quickly lead to regime's downfall too? What a timeline. Next up, for those who thought that data centers in space were just a marketing ploy, SpaceX has officially launched an AI chip into space as a first step towards building an orbital data center network. A successful test flight on Thursday used a Falcon 9 rocket to place a satellite containing four Google TPUs into orbit. This is the first launch

Starting point is 00:06:23 under Google's Suncatcher project, a collaboration between SpaceX and Planet Labs announced last November. Suncatcher aims to determine whether operating solar power data centers in space is feasible with current technology. Now, 4TPUs is, of course, an insignificant amount of compute for any real purpose, so this is purely a stress test for the chips and other components. So far, the test looks good. Travis Beals, the senior director of Project Suncatcher, reported that their team has communicated with the satellite and everything is operating as expected. Beals wrote, this is the first step in a long-term research moonshot exploring whether space could one day host scalable machine learning infrastructure. Over the coming weeks, we'll gather in-orbit data on how our TPUs handle the physical

Starting point is 00:07:00 stress of space and the radiation and thermal extremes of space. Some things can only be tested in space. As we begin, our experiments, we'll use what we learn to refine our designs, and we're excited to share more as the mission unfolds. Now, in terms of the scope of this testing, the TPUs will only operate in 15-minute bursts to avoid straining the satellite's power and thermal management systems. If the ship's function, the next step will be to launch a pair of larger satellites to test heavier workloads and laser-based networking technology. Keep in mind, this is a multi-year, if not multi-decade endeavor that relies on multiple technological breakthroughs. In the medium-term, Google plans to launch a network of 80 TPU satellites and is taking meetings to design a mega-satellite

Starting point is 00:07:38 the length of a soccer field. Hitting this kind of scale is impossible with the Falcon 9, so the project hinges on the success of SpaceX's starship, which reached orbit for the first time in September. Explaining the ambition and scope of the project, Beale said, if five years from now, everything we've done has worked perfectly, it probably means we've not taken enough risk and we've not learned as much as we could. If we're really successful with this in the long run, this will ultimately be boring and people won't think anything of the fact that their Gemini query might be getting served in space. Moving over to AI safety drama, OpenAI has fired three employees on their safety team for mishandling corporate information. In a statement, OpenAI said, we have parted ways with three

Starting point is 00:08:16 individuals for violating our policies on accessing and handling sensitive company information. Our investigation confirmed that these individuals mishandled sensitive information outside established company procedures, violating our policies and breaking the trust essential to our work. Sources speaking with the information added that the matter involved sharing sensitive information with an outside organization that does AI evaluations. Now, given how contentious everything surrounding AI safety is right now, and given how little we know of the details, there are, unsurprisingly, a huge range of interpretations when it comes at these particular dismissals.

Starting point is 00:08:50 Some wondered whether this should be read as retribution against whistleblowers. And Code AI General Counsel Nathan Calvin was concerned that OpenAI had let go of skilled safety researchers. After their names were leaked, he noted that they were lead authors on academic work around chain of thought monitoring, commenting. Them leaving Open AI right when safety monitorability is collapsing is terrible. Others had less sympathy for breaking corporate policy. With Mark Kretschman writing,

Starting point is 00:09:13 Working on AI safety doesn't give you a free pass to leak confidential information. The label isn't a moral exemption from the rules everyone else has to follow. If the allegations are accurate, firing them seems entirely reasonable. From the outside, it is extremely hard to know how justified OpenAI was in removing these employees. In large part because we don't know what information was leaked. My guess is that mostly the interpretations of this are going to be based on what one thinks about AI safety. If one is extremely concerned about safety issues, then Open AI firing people for sharing their concerns with independent third parties feels unjustifiable. On the other hand, there are going to be lots of people who agree with OpenAI

Starting point is 00:09:49 that being concerned about AI safety doesn't give you carte blanche to share confidential company matters with an independent organization outside the existing pathways for doing so. I think what perhaps many of us could agree on, regardless of where we sit on that spectrum, is that this is a good reminder about why it is important to get the formal channels for reporting and third-party evaluation up and running as soon as possible. Lastly today, in case you haven't had enough to do trying new models recently,

Starting point is 00:10:14 it appears that the first sightings of Fable 5.5 are starting to show up. The rumor mill is suggesting that some anthropic users are getting routed to a model with an updated knowledge cutoff and some slick new design taste. Chubby on X posted, Here we go, numerous users are reporting that their queries are being routed to Fable 5.5. It was only a matter of time. Get ready, the best model in the world is about to be released. Adds token Gremlin. The first Fable 5.5 results look incredible.

Starting point is 00:10:40 Expect a seriously powerful model, especially for design, 3D work, and spatial intelligence. The funny part is that Opus 55 is already such a monster that it may actually make the jump to fable feel a little less dramatic than it really is. Something to look forward to for the week to come, but for now, that is going to do it for today's headlines. Next up, the main episode. A new study from KPMG in the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than 500 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

Starting point is 00:11:25 they knew alone, but by how they worked with AI. Learn more about what separates AI amplifiers from everyone else at KPMG.com slash US slash AI amplifiers. Here's a harsh truth. Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized. Half of companies have AI tools, but only 12% use them for business value. Most employees are still using AI to summarize meeting notes. If you're the one responsible for AI adoption at your company, you need Section. Section is a platform that helps you manage AI transformation across your entire organization.

Starting point is 00:12:00 It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value. The result, you go from rolling out tools to driving measurable AI value. Your employees move from meeting summaries to solving actual business problems, and you can prove the ROI. Stop guessing if your AI investment is working. Check out section at sectionaI.com. That's C-I-O-N-AI.com. Every episode, I talk about the competition between OpenAI, Anthropic, SpaceX AI, Google, and Meta.

Starting point is 00:12:33 And if you've been listening for a while, you might have a favorite. Maybe you think OpenAI and Anthropic can stay ahead, or perhaps Meta's open-source strategy can win out. Whatever your view, every AI lab creates a different investment opportunity. Harbor Capital Advisors AI Lab ecosystem ETF suite lets you invest in the ecosystem behind the AI lab you believe in. Search Harbor AI Lab ecosystem ETFs wherever you invest or follow at Harbor Capital on X to learn more. Visit Harbor Capital.com for a prospectus containing investment objectives, risks, fees, expenses, and other important information. Read and consider it carefully before investing. Risks include principal loss and artificial intelligence-related

Starting point is 00:13:07 risks. Harbor ETFs are distributed by Foreside Fund Services LLC. Harbor is not affiliated with AI Daily Brief and the funds are not affiliated with sponsored by or endorsed by any AI lab. This is a paid advertisement and not personalized investment advice. Investing involves risk, including possible loss of principle. When I'm in a meeting, I'm fully in it. I'm thinking about the iteration and creative back and forth it takes to actually push a goal forward. What I'm not thinking about is capturing takeaways, tracking to-dos or any of that. And that's where Granola comes in. Granola is an AI-powered notepad that captures what happens in your meetings and turns it into clean, structured notes, with the decisions and action items pulled out and easy to find.

Starting point is 00:13:46 There's no setup and no configuration. It just fits into how you already work. For me, it means I get to stay in idea mode and Granola make sure those ideas actually become action. Once you try Granola on a first meeting, it is hard to go without. You can try it totally free at granola.a.ai slash brief. That's granola.a.i slash brief to get your time back. Welcome back to the AI Daily Brief. There have been so many releases recently between new models, new form factors like all these personal agents, though we haven't had a chance to catch our breath and get the latest read on how AI continues to be adopted in the enterprise. Now, let me make a pitch for why this should matter to you.

Starting point is 00:14:27 It's obvious if you work inside the enterprise, getting a sense of where other companies are, what's working for them, what barriers they're facing for adoption. All of that can be critical insight that's valuable in helping you understand where your organization sits on the adoption spectrum. and what you might need to do differently. But for those who aren't in a big enterprise, for those solopreneurs among you or freelancers, two reasons why I think this is worth paying attention to. The first is that likely many of you interact with these companies, perhaps as a service provider, and so understanding where they actually are and what they're going through becomes valuable in that way. But for everyone else, enterprise adoption is going to have a dramatic impact

Starting point is 00:15:01 on many parts of how AI evolves that matter far beyond just the enterprise itself. Take, for example, the latest data published by Ramp. In this week's AI Index, they found that token spend fell 5.2% from last week. Now, interestingly, token volume was up, meaning that companies were able to grow their use of AI while still decreasing the cost to use that AI. Rampleet economist Eric Karazian also pointed out that this was not about open source models, which remained less than 5% of business spent. The decline, he said, is driven almost exclusively by competition between OpenAI and Anthropic.

Starting point is 00:15:36 Meanwhile, one of the most underappreciated announcements from this week's OpenAI Debday was that OpenAI now has a marketplace feature for enterprises where companies can use their spend commitments not on OpenAI tokens, but on other open models sold through OpenAI. So what enterprises get is that multi-model flexibility that they are increasingly looking for and the ability to take advantage of cheaper open-weight models. What Open AI continued to get is customer lock-in and the ability to keep spend in their ecosystem even if it's flowing to other token merchants. All of that impacts how these different model providers compete with one another,

Starting point is 00:16:10 and of course how the market views their prospects. If Wall Street investors become convinced that volumes up but spend down is the permanent trend, you better believe there's going to be implications for how much they're willing to backstop and fund infrastructure buildout. So with all of that said, let's come back to some recent data from where enterprise adoption is right now. And for this, we're turning to a consistent source that we use, which is KPMG's AI Pulse survey for Q3.

Starting point is 00:16:33 The Q3 Pulse was based on a survey of more than 2,100 senior leaders across 20 different countries and shows a fairly significant maturation in enterprise AI strategy. Now, one of the things that's really interesting that KPMG has started doing is breaking out results based on where organizations are in their AI journey. What I mean by that is that they're comparing the responses of organizations that are still in an experimentation phase compared to those who have already established ROI from their AI investments. And the gap between those organizations that are still experimenting and those with established

Starting point is 00:17:06 RLI reads like a map of where business AI users will go in general over the next several months. And a lot of the types of things that those established RUI organizations are doing and paying attention to are the things that we discuss on the show all the time. To take an easy example, among those organizations with established R.O.I., 48% are seeing significant employee adoption of AI agents. This should come as no surprise. 26, as I have said numerous times was the year that agents became real, and if the people participating in both our free and paid programs or any indication, this is very much a phenomenon that is impacting the enterprise. And yet there remains a huge gap between the organizations that are farther along in the organizations that are still

Starting point is 00:17:44 in experimentation phase, with those experimenters seeing only 15% with significant employee adoption of AI agents. Now, in this era of agents, obviously cyber defense is becoming more important, and 58% of those mature established ROI organizations are putting into place AI-assisted cyber defense right now. That's a 50-point gap with the experimenting organizations where just 8% are doing any sort of AI-assisted cyber defense. A full 86% of organizations with established AI ROI have a formal AI harness layer. In other words, are maintaining some sort of interface through which their people are interacting with AI.

Starting point is 00:18:19 Once again, we see a 55-point gap, although even among the experimenters, 31% have a formal AI harness layer as well. And when it comes to all these questions that we've recently been discussing around data sovereignty and how organizations feel about handing their data over to the model labs, and whether they're going to build more complex architectures that allow them to avoid some of those issues, 53% of the established ROI organizations do report having an enterprise-wide sovereignty strategy compared to just 8% of the experimenters. The way that KPMG sums up the big difference and the big shift right now is that we're moving into a phase where the most important thing

Starting point is 00:18:56 is the management layer that sits on top of AI and make sure that all of this works inside the confines and context of the organization. 53% place accountability for AI-informed decisions at the C-suite level. And interestingly, we're starting to see some of the efficiency and cost concerns show up in the management layer as well,

Starting point is 00:19:14 with 23% having built some model routing capability. Across all dimensions of AI management, from experimentation to strategic planning, to scaling, to driving adoption, to establishing ROI, The more mature an organization gets, the more likely it is that they have a formal cross-functional or formal enterprise-wide approach to that particular issue. But what are they using this new infrastructure to do?

Starting point is 00:19:36 Productivity still remains a key goal of AI, but is actually down from being reported as a key goal by 42% of organizations in Q1 of this year to 37% of organizations in this year. Instead, a lot of what KPMG calls operating priorities are gaining ground as the important initiatives that AI is meant to address. Human AI collaboration jumped four points from Q1 to Q3, responsible AI in governance, and trust and security also jumped four points, and adaptability and resilience jumped three points. And when it comes to our perpetual question about efficiency versus opportunity AI, KPMG writes, a quarter of organizations report developing multi-agent systems and a fifth are orchestrating multiple agents. The purpose is broadening as they do.

Starting point is 00:20:18 revenue-focused agent strategies have risen since Q1, while efficiency-focused strategies have declined, and most organizations now pursue the two together rather than trading one-off against the other. And it makes sense, then, that alongside that maturation of the way that they think about AI use cases, there's also a maturation of cost management. KPMG characterizes it as moving from cost control to thinking about things in terms of AI economic management. Cost visibility, they write, is widespread, linking it to value is the next step. For those organizations that are experimenting, they are obviously not linking it to value yet. That's the whole point about why they're identified as pre-established ROI.

Starting point is 00:20:55 But for those organizations that have established ROI, 48% report that they consistently assess value against cost. Among the experimenting organizations already 43% have AI cost monitoring dashboards, a number that jumps to 77% among the established ROI orgs. At the point of AI approvals, 50% of experimenting firms have a cost review, compared to 73% with established ROI, and usage or token budgets are also showing up. 31% of the experimenting organizations have them,

Starting point is 00:21:21 with 46% of the established ROI organizations having some sort of usage or token budget as well. One of my big soapboxes is, of course, that I think that being overly restrictive with usage or token budgets when you were in that experimentation phase can be fairly limiting, but of course it's hard to tell exactly what people are considering usage or token budgets

Starting point is 00:21:38 without seeing the individual programs. Overall, the survey tells the story of AI enterprise adoption as one of increasing organizational maturity, managing the economics of AI, building better systems for monitoring AI, thinking about questions like sovereignty and multi-model architectures. If you go back a year ago,

Starting point is 00:21:57 to the Q3-20205 Pulse Survey, most of these considerations weren't even on the radar. KPMG was still asking things back then like what percentage of organizations had even tried an agent. Now all of these things are mission-critical management decisions, and I think that's extremely positive for the industry as a whole.

Starting point is 00:22:13 And by the way, for those who are worried that the focused on cost efficiency is going to lead to decrease spend, KPMG found that the average planned AI investment over the next 12 months jumped from 186 million in Q1 to 210 million in Q3. I personally think that we are still barely scratching the surface of what we will ultimately spend on intelligence. But 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.

The AI Daily Brief: Artificial Intelligence News and Analysis - What the Best Business AI Users Are Doing Different

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