How to Help People Thrive with AI

Today on the AI Daily Brief, how to help people thrive with 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, Robots and Pencils, Blitzy, Section, and Airtable. To get an ad-free version of the show, go to patreon.com slash ai daily brief, or you can subscribe on Apple Podcasts. To learn more about sponsoring the show, send us a note at sponsors at AIDealyBrief.ai. The big theme of this week has been models. Models, models, and more models. And yet, all the models in the world aren't going to help people learn how to get value out of AI. Yes, model improvements can deal with fail cases from previous models and open up new opportunities, but if people aren't supported in learning how to use them, it's kind of all for naught. And that certainly seems to be what today's sponsor section found with their most recent AI proficiency report. The story the report tells is one that will be very familiar for many of you guys who work inside big companies. Their first key finding they summed up, agents are here, agentic readiness is not. While 69% of workers they surveyed reported that their organization had taken some action on AI agents, only 16% actually use an agentic tool at work, and less than 10% can define an AI agent in their own words. This isn't surprising when you find out that only 30% of employees at organizations with AI agents have actually received agentic training. Now, this study is the latest to show this sort of detail, but is far from the only one out there telling this story. Where we're going to end today is some ideas and examples of how to help people thrive more with AI. But before we do that, since this is a weekend big think slash long reads type of episode, I actually want to read some excerpts of this recent long form piece in the Atlantic by David Brooks called The People Who will thrive in the AI age. Brooks argues that what will differentiate people is not how smart they are, but instead their relationship to mental effort. Brooks writes, Remember when AI was going to take away our jobs and leave humans with nothing to do? So far, that doesn't seem to be happening. Researchers from ActiveTrack analyzed the digital activity of more than 10,000 workers and found that when people adopted AI, their work life became more intense, not less. The time that these early adopters spent on email, messaging and chat apps more than doubled. Their use of business software rose by 94%. Researchers from UC Berkeley's Haas School of Business found that when using AI, workers started taking on tasks that they had previously outsourced, because activities such as coding and engineering became easier to do. They squeezed in work bursts in the evening, on weekends, in waiting rooms, and wherever else they had a spare moment and AI was handy. They also did a lot more multitasking, supervising a bunch of bots doing things simultaneously. The general pattern that the research points to is that many people don't use the time they save using AI to do less, they use the time to take on new tasks. AI also seems to shift workers' expectations and their bosses' expectations about how much they should accomplish in a day. Every hour feels more crowded but also more frazzled. The ActiveTrack researchers found that the time people spent on focused, uninterrupted work fell by 9%. There's even a name for this mental state. AI brain fry. Now, taking a pause from Brooke's piece for a minute, there is a lot of this feeling going around. MidJourney founder David Holes recently tweeted, my friends are all feeling extremely productive and also extremely drained with the latest coding models. This makes me feel like something is wrong, and also that there might be a big opportunity. Does anyone have any strategies they use to make it feel better day to day? This is also something I've talked about a lot. A couple months ago in an episode, I introduced the idea of the infinite backlog, basically this never-ending list of work that ensures that there is always a next thing to do. Now in the pre-AI world, while the list was never ending, there were reasonable stopping points on that list. What changed with AI and agents specifically is that now that you can effectively duplicate yourself through agents, it feels as though there should never be any downtime in work. Agents don't need weekends, they don't need sleep, so can't they be taking on that infinite backlog constantly? Of course, in reality, the limits have just shifted from how much we can do to how much planning and oversight we can support. In any case, back to Brooks, he writes, a guiding principle of the emerging AI age is this. When intelligence is plentiful, volition is valuable. The people who are going to make a difference are not the ones who seek relaxation and passively use AI to work less. They are the ones who will seek improvement and actively wrestle with AI to develop their own mental capabilities and accomplish more. In other words, what will differentiate people is not how smart they are, but their relationship to mental effort. Right now, some people have what psychologists call a high need for cognition. They enjoy thinking hard. These are the people who enjoy playing difficult games and reading dense books. On the other end of the spectrum, there are the cognitive misers, the people who find it unpleasant to think hard and take any opportunity not to do it. In the middle are the people who have a medium need for cognition. They will put in the effort when they really care about something, but they don't intrinsically enjoy it. Need for cognition correlates with intelligence, but is not the same thing. We all know a lot of really smart people who don't like to work hard. And this leads Brooks to start to identify a number of different archetypes for people who will have different experiences with AI. The first category he calls productive passengers. These are the folks who, as he describes it, have a low need for cognition, and who because of that, will try to find ways to use AI to do less. Now, this does not mean that AI won't be valuable for them. In fact, it will be valuable for them exactly because it makes tasks easy enough that they can be more productive. The challenge, writes Brooks, is that AI might actually diminish their capabilities because of how easy it makes tasks. He points to research from the MIT Media Lab that found people's brain connectivity declines as much as 55% when they are using ChatGPT compared to when they are not using it to perform similar tasks and another study from Possibility Sciences which found that gamma wave activity a sign of cognitive effort dropped by roughly 40 when people were using AI And in his estimation concerningly this reduction in cognitive activity he thinks will have predictable effects on people's thinking skills, i.e. it will make them worse at critical thinking. The second category of people that Brooks talks about are the reluctant optimizers. These he describes as people with a medium need for cognition, who understand that AI might hollow them out. They will resolve earnestly and with good intentions, he says, to not let themselves fall victim. But in the crowded and stressful rush of everyday life, they will get sucked in, their resolve will fail, and they'll become over-reliant on the bots. And the problem, he suggests, is around the relationship to effort. He writes, if you're going for optimization, you're looking to maximize output, not excellence. In a survey conducted for the software firm GoTo, 43% of workers said they had submitted AI-generated content even though they suspected it contained errors and was generally of low quality. The core problem with optimization, Brooks writes, is that it will change people's attitude towards effort itself. Chris Seibin is the head of school at Rivendell, a small private school in Northern Virginia. One day, he showed his students a film that took more than 200 artists more than five years to make. The students were baffled. Why do that? As one student put it, AI could have done it in five minutes. Seibin called this the industrialization of detachment. He argued, Brooks writes, that a student who has wrestled with a hard text, revised an argument under pressure, and failed and tried again is more than informed. He is more solid. The third category Brooks calls the mental marathoners. And in fact, he uses marathon runners as a comparison point. The automobile, Brooks writes, is a perfectly good technology for traveling 26.2 miles. There is no practical reason that any person should train themselves to run the distance. But some people do. They want to put in the effort because they want to accomplish things. They want to expand their capacities. High need for cognition people are like this when it comes to thinking. In the age of AI, Brooks writes, I suspect that the mental marathoners are going to work really hard to resist AI entropy. They're going to feel a strong desire to be original. Marathoners are going to want to produce work that feels personal, that reflects their unique self. They're going to want to find ways to use AI to increase their agency rather than diminish it. Now, so far, the essay has been fairly bleak. But, as Brooks rightly points out, while I've been treating the need for cognition as some sort of ingrained trait, and although willpower has some hereditary basis, it is also extremely sensitive to context. In other words, he writes, if AI has a tendency to undermine volition, humans can reform institutions to help build it up. He meditates on how the education system might change to shift the orientation from rote memorization and the types of functional outputs he has now to instead focus on things like volition. In other words, he writes, what really matters is not brainpower but the willingness to run the mental marathons that produce high-quality results. The crucial task, he writes, is to cultivate people's desire to seek out cognitive complexity. He ends on an optimistic note. If we can help people learn to want more or hunger more, they'll be willing to undertake the mental effort to do hard things, and will avoid the cognitive polarization that is staring us in the face. If we can educate people to be clear and wholehearted about what they truly love, then AI will do the calculating and the synthesizing, but humans will still define what matters, what is worth exploring, what missions we go on, and where we end up. That would produce a bot-filled society in which human dignity is preserved, and perhaps, even enhanced. I cover the capability gap between AI potential and AI reality every day on this show. Most companies are still figuring out how to start. Robots and Pencils is already launching and scaling. Agendic and Generative AI in production at large enterprises in weeks. AWS Advanced Tier, Pattern Partner more than doubled in a year. And they're hiring. 50 open roles. If you're someone who knows this moment is different, who wants to be inside it, not watching it, this is worth a look. At Robots and Pencils, the best ideas win, and the team is purposefully kept super high quality. This is the kind of place you look back on as the best decision you ever made. Take a look at robotsandpencils.com slash careers. Weekends are for vibe coding. It has never been easier to bring a passion project to life, so go ahead and fire up your favorite vibe coding tool. But Monday is coming, and before you know it, you'll be staring down a maze of microservices, a legacy COBOL system from the 1970s, and an engineering roadmap that will exist well past your retirement party. That's why you need Blitzy, the first autonomous software development platform designed for enterprise-scale codebases. Deploy the beginning of every sprint and tackle your roadmap 500% faster. Blitzy's agents ingest your entire codebase, plan the work, and deliver over 80% autonomously. Validated, end-to-end tested, premium-quality code at the speed of compute. Months of engineering compressed into days. Vibecode your passion projects on the weekend. Bring Blitzy to work on Monday. See why Fortune 500s trust Blitzy for the code that matters at Blitzy.com. That's B-L-I-T-Z-Y dot com. 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. 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 S-E-C-T-I-O-N-A-I dot com. enough is always on agents in the cloud doing real work across the tools your team already uses Marketing 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 at hyperagent.com slash aidailybrief. So where I want to take the conversation is not so much about schools and how they can change, although I agree wholeheartedly that the entire core goal of education needs to shift. What I'm interested in is how to improve people's relationship with AI in the here and now. Now, there is one section in Brooke's piece where he talks about some of the ways that those mental marathoners use AI well without surrendering their cognitive agency. A couple of the tips and things that people have found include things like asking for AI not to produce your thinking, but to challenge it once you've already come up with your own analysis and conclusions. Another suggestion is to make sharp distinctions between rote work and creative work. In other words, to let AI write functional emails, but not to let it write essays or memos. I think, though, that Brooks is missing the biggest opportunity here, which is very simply put, to not just use AI for things you can already do, but to use AI for things you can't do. Brooks, rather unhelpfully, I think, suggests that we shame people who overly rely on AI for writing. I don't know, man. I haven't turned over my email writing to AI, but do we really think that most of the corporate communications that we're responsible for writing involve within them some paragon of virtue of the effort to discuss the results of the latest meeting? I think we need to better distinguish between the value of different types of work and not be so concerned in many cases about the work that AI can take off our plates. But more than that, the people who I find, whose brains are not atrophying because of AI, but are in fact lighting up with new possibilities, are those who recognize that for as useful as the efficiency side of AI can be in getting that type of rote work off their plate, the real power, and in fact the exciting thing, is in doing things that weren't possible before. You know what's not easy, even right now? Figuring out how, if you are not a coder by background and not particularly technical, how to build an agent that can do things for you. Doing so involves a lot of humility, of asking AI how to do something, and then when it tells you how, screenshotting what it said and asking another AI what the heck those words mean. Of trying things, coming up against an error, and then having to figure out what that error means. Of feeling the power of releasing something that you never could have built before, only to have it crumble on the first touch with other people, and to feel the pang in the race as you try to fix it before anyone else shows up. Over time, the things that we know how to do become easy. And mental elasticity, just like physical workouts, comes from doing things that are uncomfortable and that we haven't done before. The point is to be not good at things, but to do them anyways until we are good at them, and then to run the cycle back all again. AI hasn't changed that, but for the successful AI users, it's changed the level of ambition around those new things that they might go try next. And I'm sure most of you listening are either one, the person among your group of friends and colleagues and family who uses AI like that, or alternatively, the person who is trying to use AI like that. And whether it's you or people close to you or the future you that you're working to be, the people who treat AI as this opportunity technology to accomplish things that weren't possible before, to stretch themselves, in other words, and stretch their capabilities, are in fact the key pillar upon which the organizational redevelopment around AI will necessarily be built. The Wall Street Journal's CIO Journal recently wrote an article about AI champions, i.e. the quote, AI superfans companies count on to convert the skeptics. The article argues that a large part of the increase in AI usage and the fighting of skepticism from non-users is reliant on this category of people inside organizations. The journal writes, on-the-ground champions are playing a key role in those increases. Through these programs, workers volunteer to receive early access to new tools, special training, and opportunities to present to senior executives. In exchange, they're asked to promote AI adoption to their colleagues and field questions through both formal meetings and informal conversations. They give the example of a law firm who has seen significant increases in the way that their employees use AI and are now formalizing a program around their 60-some champions on how to promote AI more effectively and track the success of them to do so. Now, what this article gets right is to identify this key role. But where it misses a little bit is the idea that AI champions are effectively just internal PR agents. Yes, it is useful to have people who are willing to have frank one-on-one conversations about AI and answer challenges and skepticisms. The proof is in the pudding. And the real value of champions is not in telling people how good AI is. It's in showing them what they could actually be doing if they tried. One of my predictions coming into 2026 was that we would start to see a role that I loosely called internally deployed vibe coders. Obviously, there is a huge trend towards forward deployed engineers, where companies who are provisioning AI are also placing engineers inside the organization to embed and help those organizations better integrate the technology. and my argument around internally deployed vibe coders was basically an extension of these types of champions programs where people who are increasingly using the new capabilities of AI and specifically agents, including the coding and building capabilities of AI agents, to pair and partner with business functions in ways that could help those business functions start to figure out how that new capability set could actually change how they work. In other words, I argued these would not be folks who are helping people figure out how to make their current work happen 20% faster. it would be people who would pair to help business functions figure out how to fundamentally change not only how they do what they do but even in some cases what they do And I wanted to end on a case study of one place where some version of this seems to be happening Uber CTO Praveen Napali recently tweeted, agentic AI adoption is on fire at Uber, and it's changing the way we build, not just in engineering, but across the entire company. Today, 99% of our engineers use AI tools, more than 70% of pull requests are attributed to local or cloud agents, and our engineers have built 2,500-plus agent skills across the software development lifecycle. Those numbers are exciting, but they led us to a much bigger question. How do we bring agentic AI beyond engineering? Finance, legal, operations, marketing, customer support, HR, procurement. These functions run on complex workflows that are often manual, highly nuanced, and spread across dozens of systems. You can't automate them effectively by looking at process diagrams or documentation. You have to understand how the work actually gets done. So we created something called agentic pods. The idea is simple. We handpicked around 30 of our most AI-proficient engineers, people with deep knowledge of Uber's systems, and paired each of them with a domain expert from a business function. Then we gave every pod just two weeks. Days one and two, shadow the expert, observe every step, document workflows, ask questions, build intuition. Day three, prioritize opportunities based on scale, repetition, business impact, and data availability. Days four to five, build a working agent alongside the person doing the job. Days six to nine, validate with several others performing the same work. Does it generalize? Does it actually make their job better? Day 10, ship. In just the past two months, we've run 16 agentic pods across 16 different business functions. Capital allocation across 150 cities from 15 hours to 30 minutes. Financial pacing reports from two days to 10 minutes. Marketing web quality assurance from two weeks to 50 minutes. Support workflow creation, 9,000 manual workflows to self-service automation. The productivity gains, he writes, are impressive, but what surprised us most wasn't the speed. It was how quickly engineers embedded in unfamiliar domains uncovered opportunities that had been hiding in plain sight. The biggest wins rarely come from automating one task. They come from rethinking an entire workflow. Once you redesign the workflow around AI, you often eliminate handoffs, remove unnecessary approvals, replace legacy tooling, reduce vendor spend, and dramatically accelerate decision-making. The workflow becomes the unit of automation, not the individual task. The most impactful agent skills cut across teams, orgs, functions, tools, and systems. The biggest lesson? The best AI opportunities are rarely visible from the outside. You discover them by sitting next to the people doing the work, understanding every friction point, and building with them, not for them. We're now forming a dedicated team to scale this further and go deeper. They'll deeply understand the work, redesign it from the ground up, and use AI to fundamentally change how the business operates. Now, I think this is super cool, and is a type of program that others could imitate almost whole cloth fairly right away. But what I'm interested in is not just the two-week results. I think inherently, you're going to see these types of low-hanging fruit productivity use cases surface, and that's great. Organizations should get through that as fast as they possibly can. The question then becomes how they reuse those gains. And my instinct is that while Praveen here is talking mostly about a flow where the engineer figures out what to do based on their close work with the business expert, I think if you start to institutionalize this sort of interaction pattern between engineering and technical thinking and business performers, the real benefits wouldn't be in the course of those two weeks. They'd be over the course of several months where the main locus of change would shift from the engineers doing that low-hanging optimization to the business people themselves who, influenced by the type of agentic working that they were now a part of, would start to think differently at core levels about the broader expanse of the work themselves. In other words, while the engineers might help the financial pacing reports move from two days to 10 minutes, it is in many cases going to be the business folks, newly influenced by these agentic techniques, and maybe even building and working with some agents themselves, who figure out the best way to spend the other one day, 23 hours, and 50 minutes. And in many, if not most cases, that won't be doing more of the same work. It will likely be doing new work, orthogonal work, work that was always dreamed but never possible before. And I believe it will be, in fact, those new things that are uncovered, the output not of the productivity itself, but the reinvestment of the gains of the productivity that really changes the business. Still, this is the sort of experimentation that is going to help more people and more organizations thrive in this era of AI. This is the type of collaboration that is going to not make latent cognitive relationships with effort be the only factor determining who thrives with AI. Brooks gives lip service to the idea that those intrinsic levels of motivation are not necessarily fixed. But you can almost tell, and sorry to David if I'm misinterpreting, but it feels to me like you can almost tell that he doesn't really believe it. He's giving a nice spin on things to end with some optimism, but it's clear he basically thinks that the marathoners are the only ones who survive this transition. If that is his belief, I disagree. I think that in most cases, in both education and work, we haven't really asked people for much for a very long time. We haven't stretched them. We haven't challenged them. We haven't incentivized them to be challenged. We give them discrete buckets of tasks, often to be done for nearly inscrutable reasons, and tell them success is doing those tasks in the time that they have allotted. That might be fine for corporate functioning, but it certainly doesn't maximize people's true potential. And I think if we do AI well, by which I mean actually supporting it, we will find far more potential in people to be maximized than most people realize is there. Anyway, something to chew on for the rest of this weekend, but for now, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching as always, and until next time, peace. you