These Are the Jobs People Actually WANT AI to Automate

0:00This podcast is supported by Google. Hey folks, Stephen Johnson here, co-founder of Notebook LM. As an author, I've always been obsessed with how software could help organize ideas and make connections. So we built Notebook LM as an AI-first tool for anyone trying to make sense of complex information. Upload your documents and Notebook LM instantly becomes your personal expert, uncovering insights and helping you brainstorm. Try it at notebooklm.google.com. Today on the AI Daily Brief, these are the roles that people actually want AI to automate. Before then, in the headlines, Google is now processing 1.3 quadrillion tokens each month.

0:42The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

0:53All right, friends, quick notes before we dive in. First of all, thank you to today's sponsors, Gemini, Robots and Pencils, Notion, InsightWise, and Super Intelligent. 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 if you are interested in sponsoring the show, send us a note at sponsors at aidailybrief.ai. Right now, there are still some good slots open in Q1, but I have a feeling that that is going to turn very quickly. So if you are at all interested, and even if you just want some information, again, shoot us a note at sponsors at aidailybrief.ai.

1:24Welcome back to the AI Daily Brief Headlines Edition, all the daily AI news you need in around five minutes. We kick off today with an update from Google where that company is now pumping out 1.3 quadrillion tokens a month to serve their AI products. You might remember back earlier this year when between May and July, we saw this massive inflection point where Google went from processing 480 trillion tokens in May all the way up to 980 trillion towards the end of July. That was monthly tokens, by the way. So about 104 % growth in just a couple of months. My speculation was this was due in part to the expansion of actual deployment use cases, particularly around AI coding, that was just consuming a huge amount of additional tokens.

2:07But whatever was driving it, it's clear that usage of AI is going up, up, up. Now, Google DeepMind CEO Demis Hassabis recognized that at this scale, the numbers are frankly getting a little bit difficult to comprehend. A quadrillion has 15 zeros in it. And to reframe that 1.3 quadrillion number, he says that's 500 million tokens a second or 1.8 trillion tokens an hour. Now, this is not the only indication that Gemini is undergoing some serious growth right now. The latest edition of SimilarWeb's traffic report showed that Gemini was by far and away the big leader for AI platform growth in September.

2:43The Gemini web app saw a 46 % jump in traffic, which was more than triple the increase for perplexity, which was in second place with a little over a 14 % jump. That report, by the way, also noted that DeepSeek notched its first month of growth since February, and also that traffic to the Grok web app was the only one that had dropped, falling by 7.4%. Now, similar web stats are a very light touch metric and not something that we should overly index on. This looks only at traffic to web apps, doesn't really reflect usage at all on mobile apps. And for something like Grok, it gets most of its use through the X platform, so we actually don't know what the overall usage of Grok looked like last month versus in August.

3:22But still, with all that said, what's undeniable from the report is that Gemini is growing at a tremendous rate. Between that and it overcoming ChatGPT in the App Store for a time before Sora kicked it all back up, the race between ChatGPT and Gemini keeps getting tighter and tighter. Plus, as the AI for Success account points out, I wonder what will happen when they release Gemini 3.0 Flash and Gemini 3.0 Pro in a few weeks. Next up today, if you thought the risk of Mark Zuckerberg poaching your big talent was over, think again. Meta has poached another very high-profile AI researcher to add to their superintelligence lab.

3:55The Wall Street Journal reports that no less than a founding member of Thinking Machines Labs, Andrew Tulloch, has left to join Meta, informing co-workers of his decision on Friday. Tulloch left OpenAI in 2024 to found TML with Mira Mirati and several other departing OpenAI leaders. Prior to joining OpenAI in 2023, he had spent a decade at Meta as a machine learning engineer. Confirming his resignation on Saturday, a spokesperson for TML said, Andrew has decided to pursue a different path for personal reasons. And according to rumors, it sounds like it may have been well over a billion reasons.

4:27Back in August, the Wall Street Journal reported that Tulloch had turned down a six-year$1.5 billion offer from Meta. The story went viral, serving as the first solid reporting that Zuckerberg was personally recruiting AI researchers and offering 10-figure deals to top talent. Tulloch was in fact the poster boy for the billion-dollar talent war that captured the narrative over the summer. Now, at the time, Meta said that the description of a billion-dollar offer was inaccurate and ridiculous, adding that any compensation package was predicated on Meta's stock rising, which frankly is a little bit of a non-denial regarding the maximum size of the comp package.

4:58Overall, that article had been focused on an overall buyout offer to Thinking Machines lab, which was turned down. The tone emphasized that none of the leading researchers at the startup had accepted Meta's offer. Reportedly, more than a dozen TML researchers were contacted by Zuckerberg over the summer. Now, there are a million different lines of speculation out there. The rumor that is flying around X is that this was a$3.5 billion offer. I don't know where that got started. I've seen no evidence to support it. The other, and to me, more compelling consideration that some are sharing is that this might reflect some amount of an assessment of the widening gap between the available resources in the sector.

5:34Despite being one of the most well-funded early stage startups in the history of Silicon Valley, the resources available to TML in terms of compute and infrastructure are a tiny sliver of what is available to Meta. Ultimately, we don't know if that's the reason or personal compensation or something else entirely is the reason these moves have happened. But as many people are pointing out, Lama 5 better deliver. Moving back over to Elon's world for a minute, XAI is joining the race to develop world models. The Financial Times reports that XAI hired a pair of researchers away from NVIDIA over the summer to work on the technology.

6:08NVIDIA, through its Omniverse platform, has been one of the leaders in practical world models used to train embodied AI in simulated environments. Google and Fei-Fei Li's world labs have also made significant progress, though their demos have been more focused on generating interactive video. Through Tesla's cars and robots, XAI could have an opportunity to pair world models with actual embodied AI. But that said, Elon Musk appears to be thinking about a different application as well. Posting last week, the XAI Game Studio will release a great AI generated game before the end of the year. XAI is currently hiring technical staff for an Omni team, which quote, creates magical AI experiences beyond text, enabling understanding and generation of content across various modalities, including image, video, and audio.

6:49Among the roles is a video game tutor who will teach Grok to produce video games. The goal, it says, is to allow users to explore AI-assisted game design. Some think this is a clever short-term play from Elon. Phil Truby writes, classic Elon strategy. World models are proving to be needed for robots like Optimus, but Optimus revenue is years away. However, world models can also be used sooner for AI-native video games. Thus, Elon is creating near-term revenue for this otherwise long-term technology. Now, one of the things always lurking behind people's minds is will there come a point where Elon decides that it makes sense to try to fold everything all together under, for example, the banner of Tesla.

7:25In that light, could this be a medium-term play to create a narrative that Tesla should buy out XAI? Remains to be seen, but regardless, it is super interesting that XAI is jumping into the world model space as well. Lastly today, escalation in the chip war as China cracks down on NVIDIA imports and the Dutch government seizes a Chinese chipmaker. If you're paying attention to the broader market at all, you will not need me to tell you that trade war tensions hit a fever pitch this weekend in the lead-up to talks between the Trump administration and Beijing. AI chips were just one front in the all-encompassing trade war.

7:56On Friday, the Financial Times broke news that Chinese authorities had begun a crackdown on firms importing NVIDIA chips. They wrote that customs officers have been mobilized at major ports, searching for H20 and RTX Pro 6000D chips that are designed to meet U.S. export controls. One source told the FT that Chinese authorities were also looking for more advanced chips that were smuggled into the country in breach of U.S. policy. In the West, we had heard that Beijing had discouraged quote-unquote firms from importing NVIDIA chips, but it seems that that was a little more than a suggestion. Alongside cargo searches, officials are also poring over documentation to see if firms made false declarations about importing NVIDIA chips in the past.

8:31In a strange twist, Beijing now appears to be far more concerned about stopping the flow of advanced AI chips than even the biggest China hawks in Washington. Then, breaking overnight on Sunday, the Dutch government has seized control of a Chinese-owned chipmaker. Nexperia is a Dutch subsidiary of Wing Tech Technology, which specializes in the production of high-volume, low-end chips for automotive and consumer electronics. On Sunday evening local time, the Dutch Minister of Economic Affairs revealed that the Goods Availability Act had been invoked in September to seize the company, the first time that that 1952 law had ever been used.

9:03He said that the move was made in order to "...prevent the situation in which the goods produced by Nexperia, finished and semi-finished products would become unavailable in an emergency. A government statement said that the highly exceptional decision had been made after the ministry observed recent and acute signals of serious governance shortcomings and actions. Wingtech responded in a now-deleted WeChat post, the Dutch government's decision to freeze Nexperia's global operations under the pretext of national security constitutes excessive intervention driven by geopolitical bias rather than a fact-based risk assessment.

9:35Endgame Macro writes, what's happening with Nexperia goes way beyond a simple regulatory move by the Dutch government. This is a frontline moment in the global tech power struggle between the West and China. On paper, the Netherlands says it's stepping in because of administrative shortcomings and national security risks. But in reality, this is about cutting off one of China's quiet backdoors into Western chip technology. NXperia may be based in Europe, but it's owned by China's wing tech, and the fear is that valuable know-how could end up back in Chinese hands. Whatever the case, it is a major escalation and just shows how many dimensions to this crazy AI story there are right now.

10:08That, however, is going to do it for today's AI Daily Brief Headlines edition. Next up, the main episode. Small, nimble teams beat bloated consulting every time. Robots and Pencils partners with organizations on intelligent, cloud-native systems powered by AI. They cover human needs, design AI solutions, and cut through complexity to deliver meaningful impact without the layers of bureaucracy. As an AWS-certified partner, Robots and Pencils combines the reach of a large firm with the focus of a trusted partner. With teams across the U.S., Canada, Europe, and Latin America, clients gain local expertise and global scale.

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12:59The result is an AI action map with high potential ROI use cases and specific change management needs, basically everything you need to go actually deliver AI value. Go to bsuper.ai to learn more. Welcome back to the AI Daily Brief. As time goes on and people get more acclimated to AI simply being a part of the society that we live in, the discourse has naturally shifted from high-level binaries and generics like, will AI take our jobs? Or even silly little aphorisms like, your job won't be taken by AI, but by someone using AI, into deeper analysis around where AI is actually useful, how it's evolving, and importantly, more recently, where people want AI to be involved.

13:41You might remember this study that we covered earlier this year from Stanford that divided tasks into four different zones based on how good AI was at doing those things and how much workers wanted them to do those things. There was a green light zone, which was things that AI was good at and that people were very excited for AI to do. A red light zone, which was things that AI was good at, but that workers didn't want AI to do. A yellow zone, which was things that people wanted AI to do, but where capabilities were a little bit low. And then a low priority zone, which was where AI couldn't do things or they were particularly hard and where people didn't really want AI to do those things.

14:17Now, this was super interesting to me. It's become a part of basically every keynote that I give, because again, it gets out of these binaries and starts to get into actual expressed human preferences from people on the ground who don't really have time for all these big philosophical debates. They're just trying to figure out how AI is or isn't going to be useful in their own jobs and what it's going to mean for their careers moving forward. Well, now we have something that almost forms an interesting companion study. Earlier this month, researchers from Harvard Business School took a fresh look at AI job replacement.

14:45Now, instead of coming from the angle of how capable AI was at performing certain tasks or trying to figure out how many jobs AI would replace, they instead asked people broadly how they feel about AI stepping in for humans in various occupations. In the abstract, the researchers wrote, Despite cultural anxiety about artificial intelligence displacing human workers, we find that Americans show surprising willingness to cede most occupations to machines. Given current AI capabilities, the public already supports automating 30 % of occupations. When AI is described as outperforming humans at lower cost, support for automation nearly doubles to 58 % of occupations.

15:25In other words, where AI is competent and cheap, in many areas, there isn't all that much moral objection to AI replacing humans. However, the researchers continued that there are a narrow subset representing around 12 % of occupations, that include things like caregiving, therapy, and spiritual leadership, that remain categorically off-limits, because automation in those areas is seen as, in their words, morally repugnant. They write, this shift reveals that for most occupations, resistance to AI is rooted in performance concerns that fade as AI capabilities improve, rather than principled objections about what work must remain human.

16:04Now, the chart that got shared all over the internet was this one, another four-quadrant chart. On the x-axis, we have technical feasibility, i.e. the percentage of tasks that are exposed to AI, and on the y-axis, we have what they call moral repugnance towards AI. The four quadrants then are in this case, the green quadrant is in the lower right. That's low moral repugnance towards AI and high capability. This is their no friction quadrant. Above that, in the capable but repugnant, which is sort of like their yellow light category, which they call moral friction. That's where there's high AI capability, but also high moral repugnance towards AI.

16:42Their red quadrant is where there is dual friction, low capability and high repugnance. And their blue quadrant, which is sort of like the opportunity quadrant of the other study that we just saw, is called technical friction, where there is moral permissibility but low capability. So let's talk about some of the types of jobs that are in each of these areas. And let's do it in that order. On the no friction side, where there is high capability and low repugnance, there are a lot of white-collar jobs. Search market strategists, financial quantitative analysts, economists, special effects artists.

17:15All of those are in the green quadrant. Now, again, I will remind you that while the other study is about what workers in those areas thought, this is about what the broader public thinks. In other words, this is the broader public looking in on other people's jobs and saying whether they're fine with those jobs being automated. It's not people self-assessing. In the next quadrant, the moral friction quadrant, where AI is capable, but there is higher moral repugnance, some of the call-out examples include sociologists, history teachers, fraud examiners, OBGYNs, legislators, and school psychologists.

17:49Basically, even if ChatGPT can theoretically give advice to students, that's not really something that people are super stoked on. In the dual friction category, where there is both low capability from AI and high moral repugnance, they have nuclear technicians, oral surgeons, bailiffs, and nannies. Apparently, even if we solve the problems of humanoid robots, people aren't willing to give their kids over to them just quite yet. And then over in the blue area, which again is sort of an opportunity area of technical friction, where there is moral permissibility but low capability, they have things like semiconductor technicians, cashiers, mail sorters, gambling dealers, and conveyor operators.

18:24Of course, if you are a startup who is thinking about areas where you could vertically design AI solutions without people being mad at you, that blue area might be a place to look. Now, Eric Brynolfsson, one of the authors of the earlier task-based study, saw this new one and made the comparison directly, saying, it's interesting to compare their chart with what we found when we asked the workers themselves what they want. Matt Bean from MIT Sloan actually ran the two studies through ChatGPT to compare with Bringerson of the Stanford Digital Economy Lab then synthesizing the analysis into a table.

18:54This new four-quadrant chart had on the x-axis, worker automation desire, and on the y-axis, public moral acceptability. So for this then green light, which was high moral acceptability and higher worker automation desire, that was things like scheduling and reminders, payroll error fixes, records upkeeping, standardized reporting, and database maintenance. It will not surprise you at all, especially if you listened to yesterday's episode about where we're starting to see AI deployed. These are in these areas where there's high value to getting it automated and people very much not protective of those areas.

19:25The next quadrant, where there is public moral acceptability, they called augment carefully or co-pilot by default. In other words, a human using an AI is probably the way to go versus handing it over to an agent. that included things like assign and allocate stories, film editing and cuts, graphic layouts, and find a unique fact research. I think this is a really revealing quadrant, because if you take a step back, one of the things that these studies are telling us is that workers have a higher threshold for what they want in their job automated, as opposed to people outside their job. People outside of their job asked whether certain tasks within their job are okay to automate, basically are in many cases saying, sure, why not?

20:05Even though the people who are doing those jobs say that there's something important or distinct about the human touch that they want to preserve. I think that's why you're seeing things like film editing and cuts, where the people who are doing that understand what makes the difference between a really great version of that and an only okay version of that. Whereas from the public at large who doesn't know about that craft, doesn't necessarily see it as craft, they just see it as something to get done. And version A done by a human craftsperson versus version B done by a robot doesn't particularly matter to them.

20:32One of the real interesting challenges that we will face as a society is how to navigate the lines between what the people on the front lines who are doing a particular job think and where broader public sentiment is. That's that quadrant that's going to see the most of that particular question. Now, over on the other side, where worker automation desire is high but moral acceptability is low is kind of the inverse of that, where workers who are on the front lines think that there's more room to automate than people who are looking from outside who find it morally repugnant. This quadrant they call assistive only and included by way of example care and therapy intake summaries.

21:07Someone pointed out that this is actually one of the areas where the Harvard study shows its limitations. Matt underscore amp on Twitter wrote, Eesh, caregiving is where automation can make the most difference if deployed appropriately. No more elder neglect while warehoused in care homes administered by underpaid overworked staff. And to try to interpret this a little bit, the broader public is saying, absolutely not. we should not have AI taking care of sick or elderly people. That is a job that is for humans. It is distinctly of humans. We should have humans doing that. That's the lens through which they're interpreting it.

21:40However, what this combined chart is showing is that the people who are in that role understand that there are parts of this that are absolutely and incredibly valuable to automate. As Matt points out, many of the facilities where this type of caregiving happens are plagued by the problems of, as he puts it, underpaid overworked staff. To the extent that AI can take off big chunks of, for example, administrative work that allows them to just stay focused on the already emotionally taxing parts of human caregiving, there are probably really big benefits to be had from that. And this is, of course, why it's going to be so important to not stay on the role-level analysis, but actually get into task-level analysis.

22:21In many ways, I think that the best way to look at AI-related job displacement is from an additive task kind of level. In other words, you take it from the task level, can AI and should AI automate a particular task? And then from there, you look at what percentage of a particular role as it's currently constituted is tasks that can be automated. So instead of saying, we're trying to automate role X, you instead say, automation can do 70 % of the work of that role. and then we get to ask at what threshold that role needs to change. Does the role stay the same, but there's just fewer of those people?