The AI Daily Brief: Artificial Intelligence News and Analysis - Can Today’s AI Really Replace 12% of Work?

Starting point is 00:00:00 Today on the AI Daily Brief, can today's AI already do 12% of work? Before then, in the headlines, what to make of these reports that Microsoft is lowering sales targets for AI. 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 sponsors, KPMG, robots and pencils, Blitzy, and Rovo. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can

Starting point is 00:00:35 subscribe on Apple Podcasts. And if you want to get in on the 2025 prices for sponsorship, now is your last chance. Shoot us a note at sponsors at AIDailyBreef.aI. Welcome back to the AI Daily Brief Headlines Edition, all the daily AI news you need in around five minutes. We have a story today that shows yet again how on-edge markets are when it comes to a potential AI bubble. Earlier this week, the information reported that Microsoft has lowered sales quotas on

Starting point is 00:01:02 AI products after many salespeople missed targets for the last fiscal year and in June. The report cited two salespeople within the Azure Cloud Division. Those sources said that adjusting quotas down is unusual for Microsoft and could reflect a lack of willingness among corporate clients to pay more for AI agents. The report stated that the U.S. Azure Sales Division had set targets to raise customer spending on Azure Foundry by 50%. Foundary is Microsoft's unified platform for developing, deploying, and managing AI applications and agents. The sources said that less than one in five salespeople had hit the 50% growth target, leading Microsoft to reportedly lower the target to 25% for the current fiscal year. In another U.S. Azure unit, the target was doubling

Starting point is 00:01:41 foundry sales, and after most salespeople failed to hit the target, the quota was slashed to 50% growth. Now, there have been a couple reports from earlier in this year of AI sales being a problem for Microsoft contributing to a narrative. Microsoft pushed back on the reporting, however, with a spokesperson stating, the information story inaccurately combines the concepts of growth and sales quota. Aggregate sales quota for AI products have not been lowered. Investment Bank Jeffries agreed that it isn't a big problem, writing in a research note that the information had, quote, completely missed the point of its article. They reported that Microsoft management urged investors to focus on accelerating performance obligations, a measure of their cloud backlog, and an

Starting point is 00:02:18 indication of future revenue growth. Jeffries added that their checks showed strong adoption of copilot. Microsoft stock was down as much as 3% in the morning, paired back losses in the afternoon, but collapsed into the close to end the day down 2.5%. Basically, in my estimation, the price action suggests that investors are jittery on any sign of AI weakness, but at the same time aren't really sure how to weigh these smaller sort of narrative shifts. There are so many different ways to take this. Mr. Longterm points out that company-specific execution is not the same as a macro sentiment shift, and that Microsoft problems are more likely to be Microsoft problems than AI demand is slowing. Some people use this as another chance to point to point that AI adoption in the

Starting point is 00:03:00 enterprise is slow and difficult. And of course, I do think it's fair to ask, whether employees actually want the current crop of agenic tools as they're designed. In other words, while there is obvious product market fit around certain types of AI, for example, chatbots, I don't necessarily think that's the case for a lot of the very prototypes of automation experiences we have. In particular, I'm pretty bearish on workflow builder automations like N8N for the average employee. People who are willing to get over the interface and UX hurdles can find a ton of value in those products, as is very clear from their growth and success. but I don't necessarily think that that's going to represent the average enterprise buyer.

Starting point is 00:03:38 Mostly what I think is that right now, the market is looking for any sign of AI weakness and pouncing on it when it finds it. Next up, we have Nvidia CEO Jensen Huang on Joe Rogan, which, as boring business points out, is like a Taylor Swift concert for people who know what a GPU is. A couple of the highlights included a discussion of the competition with China, where Huang said, we've always been in a tech race with someone. Technology gives you superpowers, whether it's information superpowers or energy superpower, or military superpowers.

Starting point is 00:04:06 When Rogan suggested that winning the AI race is a matter of huge national security, saying, when we're working towards this ultimate goal of AI, it's impossible to imagine that it wouldn't be of national security interest to get there first. Huang questioned the framing, adding, the question is, what's there? I don't think anybody really knows. In other words, he's saying that the AI race doesn't have a definitive finish line. Huang's view tends to be that the end state for AI is not about a superpower claiming dominance.

Starting point is 00:04:30 It's more about AI becoming infrastructure, fading into the background as the technology improves. and powering everything from healthcare to transportation. Over in Acquisitionland, OpenAI has agreed to acquire Neptune, a startup that builds monitoring and debugging tools for AI training runs. The two companies have already worked together to develop dashboards for training foundation models, so the acquisition will allow them to work much more closely. OpenAI's chief scientist Jacob Pachaki said in a statement,

Starting point is 00:04:55 Neptune is built a fast, precise system that allows researchers to analyze complex training workflows. We plan to iterate with them to integrate their tools deep into our training stack to expand our visibility into how models learn. Deal terms were not disclosed, but the information reports that it was an all-stock deal valued somewhere south of $400 million. Boy, I remember back in my day when the rocks were soft and $400 million was a big acquisition. Lastly today, a check-in on AI and agent performance on Black Friday, AI shopping assistants seemed to have slightly outperformed on their big Black Friday test. According to data from Censor Tower, Amazon sessions using the Rufus chatbot that resulted in a sale were up by 100,

Starting point is 00:05:34 compared to the trailing 30 days. By comparison, sessions that didn't involve Rufus only increased by 20%. Similar outperformance was evident in day-over-day stats, and Amazon noted that sessions involving Rufus outpaced total website sessions. OpenAI also saw solid results with referrals from chat GPT to retailers increasing by 28% year-over-year, according to Aptopia. Chat-GPT seemed to be favoring the big retailers even more than it used to. Amazon's share of chat-GPT referrals grew to 54% from 40.5% last year. Walmart, of referrals was up from 2.7% to 14.9%. Stats from Adobe Analytics showed that overall AI-related traffic to U.S. retail sites increased by 805% year-over-year for Black Friday. Notably, Adobe picked

Starting point is 00:06:16 up an impressive boost in converted sales. Shoppers who used AI were 38% more likely to buy than non-A.I. Traffic sources, suggesting much greater intent from AI users. AI shopping is also becoming increasingly ubiquitous, with an Adobe survey finding that 48% of shoppers said they had used or planned to use AI during their holiday shopping. According to Salesforce, AI agents influenced 14.2 billion in sales globally on Black Friday with 3 billion in the U.S. alone, making it a significant portion of the record 11.8 billion in U.S. Black Friday spending in online stores. I don't know about you guys, but I've certainly found myself using Chat Chb-T's shopping research tool a little bit more frequently than I had expected, so who knows, maybe they are onto something. That, however,

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Starting point is 00:09:52 Discover Rovo, your new AI teammate powered by Atlassian. Get started at ROV, as in Victory, O, Welcome back to the AI Daily Brief. Ah, my friends, we are once again talking about an MIT study which the headlines seem determined to get wrong. But in this case, at least, the study itself is actually much more interesting. And the reason that there is a lot of noise around it is that it is hitting on one of the central questions of the moment, which is trying to understand just how much work AI

Starting point is 00:10:24 can actually replace right now and perhaps more importantly, what sort of trajectory is it on. One of the things that we've been tracking here on the show is the increasing political acrimony around AI. This is coming from both the right and the left, and is I think all prelude in jockeying to position ahead of next year's midterm elections in the United States. So we're going to look at two different pieces of evidence around this question today of how much work AI can actually replace. One is this project out of MIT called Project Iceberg, which is generating some number of scary headlines like this one from CNBC. The MIT study finds AI can already replace 11.7% of the U.S. workforce, but then we're also going to look at the direct testimony from Anthropic in their recently released blog post how AI is transforming work at Anthropic.

Starting point is 00:11:10 So let's talk about this study first. In explaining itself, Project Iceberg writes, current AI research has focused on individual agent capabilities, building models that can read, write, reason, and create. But what happens when they interact? When millions of AI agents interact with each other and with humans in the same environment, collective behavior is shaped less by individual capabilities and more by the coordination protocols between them. Project Iceberg explores this algorithmic frontier, designing and testing coordination mechanisms for human AI populations at scale. They basically want to understand

Starting point is 00:11:42 how the hybrid workforce is going to evolve and impact the way that we do work. Now, the specific context for all of this reporting is their recently released Iceberg Indexed, which is a measure of skill-centered exposure in the AI economy, and that word, skill-centered is going to become important as we'll see. The goal of the iceberg index is to provide a better picture of automation capability that is forward-looking rather than backwards-looking. In other words, as they point out, traditional workforce metrics only measure employment outcomes after a particular disruption has occurred. They do not, as Iceberg puts it, show where AI capabilities overlap with human skills before adoption crystallizes. So what Project Iceberg did is use what they call a large

Starting point is 00:12:24 population model to, quote, simulate the human AI labor market, representing 151 million workers as autonomous agents, executing over 32,000 skills across 3,000 countries, and interacting with thousands of AI tools. The iceberg index is a skill-centered metric that measures the wage value of skills AI systems can perform within each occupation. What they found is that right now, visible exposure is concentrated around software-related work, such as software development and data science. This represents around 2.2% of wage earning skills and is basically the part of the iceberg that they say is above the surface. However, beneath the surface, they find that current AI can automate about 11.7% of current wage earning skills and that this hidden cognitive

Starting point is 00:13:08 automation, their phrase, expands the visible tech adoption around software work to cognitive work in areas such as finance, HR, and customer support. So that 11.7% is where the number from these headlines come from. Going back to CNBC, again, the headline reads, MIT study finds AI can already replace 11.7% of the U.S. workforce, representing as much as 1.2 trillion in wages across areas, including finance, health care, and professional services. Now, Project Iceberg itself goes out of its way to make clear that this is not a measure of potential job loss or employment displacement. The very first question in their frequently asked questions, says the index measures where AI systems overlap with the skills used in each

Starting point is 00:13:51 occupation. A score reflects the share of wage value linked to skills where current AI systems show technical capability. For example, a score of 12% means AI overlaps with skills representing 12% of that occupation's wage value, not 12% of jobs. This reflects skills overlap, not job displacement. The second entry in their FAQ, does the index predict job loss or displacement? No, the index reports technical skill overlap with AI. It does not estimate job loss, workforce reductions, adoption timelines, or net employment effects. They reiterate this in the abstract of the paper as well. The index captures technical exposure, not displacement outcomes or adoption timelines. And despite CNBC in their article writing the index is not a prediction engine about

Starting point is 00:14:37 exactly when or where jobs will be lost, they still use this headline, which they know is incorrect. So there are two things going on here. One is the important observation that just because the thing can be automated doesn't mean that it will be automated. There's an entire set of social structure and human and organizational inertia which can significantly slow down the adoption of any automation technology. But two, there is not a one-to-one correlation between a wage-earning skill and a job. In other words, jobs are collections of skills, not the instantiation of a single skill. I use Gemini to create a graphic to try to visualize this. What the iceberg index is saying is not that 12% of jobs are going to be eliminated. It's that 12% of tasks within all

Starting point is 00:15:16 jobs could be automated right now by current AI. The critical difference here is that part of the market adaptation that's going to happen is that which skills constitute any given role or job are inevitably going to change. If you view your job as a bucket of skills, some of which can be automated only difficulty or with more advanced AI and some of which can't be automated at all, there is likely the allocation of time and distribution towards the skills that can't be automated and away from the skills that can. Now, that does not mean, of course, that there will be no job displacement from task level and skill level automation. For example, there are some jobs that are highly concentrated around a single, highly automatable skill. There are also jobs that, although they

Starting point is 00:16:00 have a bunch of different skills, are collections of skills that are all highly automatable. Those jobs are obviously highly exposed, even if we appropriate, recognize that this study is talking about skills and not jobs. Also, it should be noted that if some meaningful portion of a job skills can be automated, even if those roles don't go away automatically, it is possible that with the expanded time that's won back from people handing over the automatable part to automation, maybe there are fewer of those roles in aggregate because the people who have been freed up for higher value tasks can do more of them themselves and don't need as much redundancy in the workforce. In other words, there could still be significant and

Starting point is 00:16:38 meaningful employment displacement, even in the context of actually appropriately understanding what studies like this are saying, it's just not the hysterical headline of 12% of jobs eliminated right away. And of course, none of this takes into account the fact that new skills are being enabled and that new roles will come online as well. One of the challenges with any new technology is that we see the destruction in creative destruction before the creation. But what about some practical evidence in reality? I want to turn to this post from Anthropic about how AI is transforming work inside that company. And I want to kick it off with comments from CEO Dario Amade at the Dealbook Summit on Wednesday, December 3rd.

Starting point is 00:17:13 There's just an exponential. Just like we had an exponential with Moore's Law, chips getting faster and faster until they could do any, you know, simple calculation, you know, faster than faster, faster than any human. I think the models are just going to get more and more capable at everything. Every few months, we release a new model, gets better at coding. It gets better at science. You know, now models are routinely winning, you know, high school math Olympiads. They're moving on to college math, Olympiads are starting to do new, new mathematics. For the first time, I've had internal people at Anthropic say, I don't write any code anymore. I don't write, I don't open up an editor and

Starting point is 00:17:48 write code. I just let Claude code write the first draft, and all I do is edit it. We had never reached that point before, and the drumbeat is just going to continue. And I, I don't think there's any privilege point around. There's no point at which the models start to do something different. What we're going to see in the future is just like, like we've been seen in the past, except more so. The models are just going to get more and more intellectually capable, and, you know, the revenue is going to keep adding zeros. So let's talk a little bit more about what they're finding around AI's impact at work inside their company. This is, of course, part of Anthropics' broader attempt to understand

Starting point is 00:18:26 AI's impact on the economy, which they call their economic index. The economic index looks both inside and outside and publishes regular research on markets, jobs in the economy. So this particular study comes from a survey of 132 anthropic engineers and researchers that was conducted in August of this year. It also involved 53 in-depth qualitative interviews, as well as looking at cloud code usage data. The TLDR, they say, is we find AI is radically changing the nature of work for software developers, generating both hope and concern. Engineers, they say, are getting a lot more done, becoming more full stack, accelerating their learning in iteration speed, and tackling previously neglected tasks. So much so that it's actually bringing up questions of whether they will

Starting point is 00:19:05 lose deeper technical competence or become less able to supervise the outputs. So some of their key findings, their engineers and researchers use ClaudeCode most for fixing code errors and learning about the code base. In other words, despite Dario talking about how some folks are completely turning it over to let Claude code write the code for them, that doesn't seem to be the norm just quite yet. Anthropic team members are definitely using Claude more and seeing more benefits. Employees, they say, self-report using Claude in 60% of their work and achieving a 50% productivity boost, which is a 2 to 3x increase from a year ago. The productivity increase is a little bit about spending less time on things, and even more

Starting point is 00:19:42 about an increase in output volume. 27% of the work done with Claude consists of tasks that wouldn't be done otherwise, and most employees say that they can fully delegate between zero and 20% of their work to Claude at this stage. Now, on the qualitative side, part of why the delegation is increasing is that they find that employees are, in their words, developing intuitions for AI delegation. They write, engineers tend to delegate tasks that are easily verifiable, where they can relatively easily sniff check on correctness, and many describe a trust progression, starting with simple tasks

Starting point is 00:20:13 and gradually delegating more complex work. They find that Claude is handling increasingly complex tasks more autonomously, the measure they have. Compared to six months ago, the complexity of the tasks tackled with Claude Code has increased, the number of consecutive tool calls Claude Code can make more than doubled, and the amount of human input needed to accomplish a given task has decreased significantly. The impacts are profound enough that it's causing a lot of questions internally around how it all shakes out. For example, they find that skill sets are broadening into more areas, but people are also worried about the atrophy of deeper skill sets. There is career evolution and uncertainty, changing perceptions with how people perceive their

Starting point is 00:20:52 relationship with their work, and maybe even workplace social dynamics changing as people turn to clawed first rather than going to colleagues. To me, one of the things that I think is going to happen over the course of the next 12 months and is going to be a hallmark of 2026 is on the one hand we're going to see a lot more academic studies, like this one from MIT, but we're also going to hopefully get a lot more of this sort of internal focus study that shows the reality on the ground. The magnitude of the potential disruption here is such that it's extraordinarily hard to predict exactly how it's going to play out in practice. There are so many more factors than just what AI is technically capable of that will determine how it diffuses throughout workplaces and the broader

Starting point is 00:21:33 economy. For now, it is really interesting to see these testimonials from the front lines of the companies that are building the technology, but that is 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 - Can Today’s AI Really Replace 12% of Work?

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