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Managing AI Agents: Pinpointing Value in the Overproductive Future of Work
The current landscape for businesses leveraging advanced artificial intelligence is shifting at a speed few can fully grasp. As described through practical experience, the arrival of faster AI agent models—such as a forthcoming OpenAI release powered by Cerebras technology promising up to 20 times the speed of existing models—signals the acceleration of productivity far beyond traditional team boundaries. The implications are tangible and immediate: individual contributors are outputting at levels that rival or surpass large, multi-person teams, but this operational leap brings clear tradeoffs, risks, and procedural shifts that business leaders must anticipate to ensure sustainable growth.
AI Productivity Optimization: The Real Impact of Faster Inference
One concept discussed was the transition from traditional solo expertise to the management of multiple AI agents running in parallel. Currently, it is feasible for a single individual to direct 50 or more AI sub-agents, each autonomously creating deliverables—from code to marketing assets—at a quality equal to or surpassing that of many human experts. The conversation focused on what happens as the average completion time for agent-driven tasks drops from five minutes to as little as thirty seconds, thanks to enhanced AI models operating at dramatically increased inference speeds 03:09.
This compression of wait time translates directly into heightened throughput: a previously linear project pipeline becomes a web of simultaneous tasks, each requiring oversight but minimal hands-on execution. As businesses scale this model, the reality becomes that workflows, outputs, and the very rhythm of daily operations shift from active production to a supervisory paradigm—monitoring both task quality and alignment with organizational objectives.
Human Expertise and Agent Collaboration: Preserving Organizational Knowledge
A key theme that emerged was the risk that this acceleration poses to traditional forms of mentorship, knowledge transfer, and skill development within teams. Specific findings from recent studies illustrate the issue: 68% of professionals now report asking AI agents first—even for routine or "obvious" questions—while only 4% turn to their managers 09:04. Once agents repeatedly deliver satisfactory results, employees increasingly bypass not just supervisors, but specialists who would otherwise have engaged in detailed collaborative exchanges.
The discussion explored how every reduction in human-to-human handoffs erodes opportunities for contextual learning, nuanced correction, and professional growth. Supporting data from a May 2026 study indicated that 63% of respondents explicitly use AI to avoid challenging workplace conversations 13:14. This pattern, if left unchecked, threatens to deskill workforces and diminish the quality of internal feedback loops which traditionally elevated product and process quality.
Middle Management Evolution: The Next Layer of Knowledge Work
Several points were raised, including the structural change in business roles: the proliferation of powerful AI agents effectively expands the middle management layer. With more professionals managing fleets of agents rather than teams of people, the locus of value shifts from hands-on execution to oversight, exception management, and knowledge curation 18:06. For instance, decision makers are now able to encode and deploy their expertise in agent form, allowing hundreds of users to access standardized, high-value outputs without direct human follow-up 19:05.
This transformation demands a strategic approach to managing which human connections and decision points remain critical—and how those are protected within new, agent-driven workflows.
AI Strategy Resilience: Building for Monthly Change
One concept discussed was the accelerated pace of business process evolution. With AI models rapidly self-improving, sometimes demonstrating double-digit percentage gains in efficiency or cost-reduction within weeks, even quarterly or annual strategy reviews may be insufficient. The new paradigm advocated is one of "unlearning and rebuilding": organizations must become comfortable revisiting their workflows, policies, and internal best practices on a monthly basis, responding to opportunities and challenges as models evolve 30:39.
A critical insight is that domain expertise, while still valuable, becomes increasingly decoupled from daily operational relevance. Instead, the emphasis falls on the ability to set agent guardrails, inspect agent outputs for correctness and safety, and continuously redesign processes to leverage expanding AI capability sets.
Human Handoffs: Safeguarding Judgment, Learning, and Belonging
To counterbalance rising efficiency with the need for sustainable culture and innovation, three explicit human "handoffs" were identified as essential to protect:
Judgment Ownership: Assigning clear accountability for addressing erroneous agent outputs ensures organizational responsibility remains defined amid automated workflows 28:16.
Learning through Explanation: Requiring that key decisions are explained and not merely accepted from AI maintains critical thinking and context-building within teams.
Scheduled Belonging: Dedicating time for coaching, recognition, and unscripted conversation upholds the social and developmental benefits of traditional workplaces 29:06.
Conclusion: Raising Ambition while Managing Compression
As faster AI agents expand the envelope of what individual employees and small teams can achieve, the business value does not lie simply in doing more, faster. Instead, it arises from intentionally elevating ambition—rethinking what meaningful work can be accomplished given exponential productivity—and architecting organizations that continuously adapt. Advantaged organizations will be those who protect critical judgment touchpoints, foster ongoing human learning, and rebuild foundational processes at a pace that matches AI’s accelerating evolution.
The immediate opportunity—and challenge—is to move beyond the promise of “productivity” and design business infrastructure that extracts real value from the convergence of human judgment and increasingly autonomous machine output.
Topics Covered in This Episode:
- Managing Dozens of Productive AI Agents
- OpenAI Cerebras: 20x Faster Agent Models
- Impact of AI Agents on Human Coworkers
- Agent-Driven Workflows vs. Human Collaboration
- Increasing Agent Reliance and Fading Mentorship
- Accidental Deskilling and Compression Tax
- Protecting Human Judgment and Learning Handoffs
- Expert-Driven Loops in AI Workflows
- Monthly Rebuilding of AI Strategies and Processes
- Middle Management Evolution in AI Native Companies
Episode Transcript
Jordan Wilson [00:00:16]:
I am the only full time employee at Everyday AI. Sure. I have a few contractors helping out, but I'm the only full time human responsible for creating this daily podcast that reaches millions of people a year. Others are kind of always shocked to hear that as most of the other top 25 tech podcasts have large teams to produce the same amount that we produce. But do you know what I do all day? I just manage agents. I mean, literally right now, I have codex running with at least 50 sub agents active. I have codex controlling Cloud Code, and I have scheduled agents running in Google Gemini, Cloud Cowork, Copilot, and others. Maybe I'm a little overly productive from a single human to output perspective, but it's about to get worse or maybe better depending on your perspective.
Jordan Wilson [00:01:11]:
That's because as soon as today or Friday at the latest, we'll have a new open AI model powered by Cerebras that runs up to 20 times faster than their current models at the same capabilities. And that got me thinking, what the heck is the future of work gonna be like? I mean, I'm already agent pilled and producing more content and hopefully value than much larger teams with many more humans. So what happens when all of my agents are 20 times faster? I'm not sure yet, but I wanted to kind of opine about that out loud on today's show and explore this weird conversions that we're in now. Because not only are agents producing economically valuable work end to end at a higher quality bar than expert humans, but now we are entering a new phase where those AI agents might be working 20 times faster. And I don't know if anyone truly understands what this convergence means for the future of work, but let's unpack it and see what we might come up with anyways on today's episode of Everyday AI. So here is the big picture. What the heck is gonna happen when millions of professionals like me and you, business leaders that are on the cutting edge of AI, what's gonna happen when we just become managers of faster agents and they become more powerful? Right now, AI is fast and parallel enough that one person can direct dozens of agents a day. That's pretty much all I do all day.
Jordan Wilson [00:02:41]:
But OpenAI as well, we are seeing maybe by the time you're listening to this, it will already be released. But at least as of now, you know, the rumors are saying that we're gonna be getting a new OpenAI model powered by Cerebras. That's essentially this really fast chip. And, essentially, let's say you put, you know, a very, you you know, smart agent out there and it thinks and, you know, normally, it takes, five minutes and you get a great output. Well, what happens when that five minutes becomes thirty seconds? So, normally, you go, I don't know, manage other agents or do other tasks during that time that you're waiting. What happens when things are just instantly done? And the upside here is is real. Right? With workers reporting more autonomy and more outputs. But the risk is maybe a workday spent managing machines while human trust and mentorship quietly just thins out.
Jordan Wilson [00:03:34]:
So on today's show, here's what you're gonna learn. You're gonna know why employees are increasingly asking agents before managers, specialists, or awkward conversations. You're gonna understand how faster inference turns solo work into supervising parallel machine teams. You're gonna know how winning companies can empower human judgment while still automating most of the manual knowledge work, and you're gonna leave the show with a three human handoffs to protect before your workflow quietly deletes them. Alright. Welcome to Everyday AI. If you're new here, my name is Jordan Wilson. We do this every day.
Jordan Wilson [00:04:07]:
It's for you. It's your daily unedited, unscripted livestream podcast and free daily newsletter helping business leaders like you and me keep up with drinking from the fire hose of new AI updates. I turn that fire hose down. I serve you nice glass of water and say, here, this is what matters today. Go take this information and share it with your team and, become really smart in AI. So that's what we do here. Hopefully, the podcast is helpful. If you didn't know, we recap it all in our daily newsletter.
Jordan Wilson [00:04:37]:
So make sure that you go check out our newsletter for that and all of the other AI news. Alright. So when did this start happening? I'm not sure. As someone that follows this every single day, this kind of convergence now that is maybe gonna start today for people on the edge of the edge. But I think the rest of the enterprise will start to feel with this in maybe six to nine months. But we are now gonna be at this convergence very quickly, where we've had these extremely capable agents now for the most part running on desktop. So whether you're using codecs, CHBT work, clogged desktop, something else. Right? But I'd say those and maybe cursor, are the big players at at least when it comes to people number of people using them.
Jordan Wilson [00:05:25]:
I don't think Google is there just yet, with, you know, anti gravity or their Gemini desktop. Microsoft CEO Satya Nadella did mention again, yesterday, they are super app coming out. So I do know that most of our, many of our listeners out there are using Microsoft Windows and and Copilot. So you'll be living in this world soon too if you haven't already, maybe with, you know, Copilot, co work. But we're at this place now where we have these agents that if you know what you're doing, they will literally on demand, output the same level, the same asset, the same deliverable as expert humans. Right? We see this in, important benchmarks like GDP valve that, you know, a human and a large language model get the same inputs, and then they create outputs. They're judged blindly by subject matter experts. And, you know, we're at the point now where AI models win or tie about 90% of the time.
Jordan Wilson [00:06:21]:
So AI models or AI agents can actually do the same work that humans do and create these assets or artifacts or deliverables, which is cool and all. Right? But, you know, I'd say the downside, no. It's not even technically a downside. Right? I always tell people, be a little patient, but you have to, number one, know what you're doing. Number two, you have to, understand, kind of agent guardrails in context engineering, which those are kind of basics, tables, table stakes now. But number three is a little bit of time. Right? And that little bit of time is now what could be changing today. Whereas before, you know, I have agents that work for many hours, but I'd say my average agent task maybe lasts about five minutes.
Jordan Wilson [00:07:05]:
So for the most part, all day, I'm just flipping between agents. Right? And one of the big things I like doing is doing that on, chat g b t voice mode. But what happens then when that five minutes, becomes thirty seconds? Right? I think of so much of what I actually accomplish, day to day. It's when I'm waiting for agents, I go check-in on other agents. So then at least, you know, by the time the day is done, right, in theory, I've had time to check-in and look at those artifacts and look at those outputs and, you know, manually approve them. So what happens when things go faster? Well, one thing is I I think where we're gonna start is workers are just gonna start asking agents now. Right? Because not only is this convergence of these two things happening, faster and smarter agents, but also that takes away, I think, ultimately, human connection. And I think where, you know, people for the last twenty years, right, maybe, I've been guilty of this managing people in the past, and maybe you've said this or it's happened to you.
Jordan Wilson [00:08:15]:
But, you know, saying, hey. Did you Google that? Right now, one of the things is gonna be like, hey. Did you ask our agents on that? Which isn't necessarily a bad thing in theory. Right? But it's gonna take away human connection. Right? Even for me. Right? I started the show by saying I'm the only employee at everyday the only full time employee at everyday AI. I'm not saying that's a good thing or a bad thing, but I'm not talking to a lot of humans all day. Right? For the most part, I'm talking with agents.
Jordan Wilson [00:08:51]:
Again, pros and cons, good and bad. I'm able to produce a lot more, but maybe I'm getting fewer outside perspectives that aren't tied to my own custom instructions and and make data that I'm feeding these models. But right now, studies show, so Adobe just came out with a an interesting study this month that showed that 68% of people, ask agents obvious questions, and only 4% of, those people would ask their managers. So people are, like, more than 15 times, 16 times more likely to ask an agent something than ask their manager. Again, not saying that's a bad thing, but what does that do to human connection? And workers chose the agent because it answers instantly and never makes them feel unprepared or dumb. And then once that agent answers first, workers kind of stop routing the work to specialists too because then they build more trust. And they're like, okay. Well, I was gonna ask my manager on this, but the agent did great.
Jordan Wilson [00:09:51]:
And I was gonna take this over to the product team, but I asked our agent, and it that did great too. Right? So, you you know, you start to be able to build faster in silos, versus maybe building more intentionally at a slower pace, but in a collaborative fashion. So Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Jenna AI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use GenAI. So whether you're looking for chat g p t training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to your everydayai.com/partner to get in contact with our team, or you can just click on the partner section of our website.
Jordan Wilson [00:11:04]:
We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GenAI. That's the other thing is now all of a sudden, we're getting a lot more skills that we didn't have before. You know, and we're able to attempt work that we couldn't previously because we have a new set of skills. You know, kind of the the the best way for me to look at this, right, way back in the day, you know, I used to have a marketing and advertising agency. Right. You know, kind of a big part of my background is, you know, I was a writer. I was a journalist before. And, you know, as a marketer, sometimes you have to wear many hats.
Jordan Wilson [00:11:46]:
But at a bigger company, a lot of times, it's you have people who are specialists. Right? You have people who just maybe write Google Ads, and that's all they've done for, you know, twenty years. You know, now it's getting to the point where one marketer, maybe they can't do it at an a rating, but they could maybe do 20 jobs at a b rating. Whereas before, maybe they only had one specialty. Right? So now, as we get collectively better in the enterprise at sharing our, our skill sets, recording, and, distributing kind of our our nuance understanding, and kind of internal IP, so to speak, and give that to others via shared agents or shared skills. Right? All of a sudden, individual humans don't always need, well, their manager's approval or, even a specialist to help complete the work. And every handoff, right, those handoffs, the human human handoff that we're starting to decrease, it deletes a human conversation. It deletes mentorship.
Jordan Wilson [00:12:55]:
Right? Handoff is kind of once something is carried through explanation, correction, introduction, and a moment of shared context. So it was Purplies. I I I believe that's how it's pronounced. Purplies? May 2026 study that found that sixty three percent of people use AI to avoid a difficult workplace conversation. Right? And and that's, you you know, that one skipped conversation multiplies. And I think you also start to get internal project drift there, but I think so much of even I'm thinking of where I am today. Right? Probably the the reason maybe why I've been able to have whatever level of success that I have with this weird little podcast thing, is, you know, I had a lot of great mentors, throughout the years. I've I've been able to learn from very smart people, and sometimes that comes from conversations when things go awry, you know, or when things go great and having that be a teachable moment.
Jordan Wilson [00:13:52]:
So I'm just thinking, you know, yes. There's, you know, working with agents, you can get all those things and build them in, but, you know, agents by default are sometimes overly sick of fancy. You you know, and then I think you can almost develop a certain, AI psychosis, you know, in the good way or bad way, versus, well, when you had a team of of humans and collaborating more with humans, I don't think that that was as big of a problem. Right? Working in these silos that, yes, maybe you are, you know, two, three, four, five times more productive in terms of the, business value that you're able to create, but at what cost? So the translation here is, well, we have more collective skills that we didn't have before, but and we also have fewer productivity roadblocks. Right? The two things that I just covered. And, well, that gives us all the reason to be more productive, and, well, things are getting faster now too. That's because, you know, today, maybe tomorrow, but, OpenAI said in July that they're going to release the new GBT $5.06 soul on the Cerebras chip, which is up to 750 tokens per second. So if you don't speak tokens, that's about 20 times faster than the current models.
Jordan Wilson [00:15:08]:
Right? So if you go into, you know, codecs like like I do and, you know, you turn on, you know, GPD56 sold and you put it on, you know, extra high or high or ultra or something like that, even if you're throwing a hard task, you know, you might only have to wait five, six, seven minutes. Now all of a sudden, that's gonna be a matter of seconds. So the value there is is the throughput. It's with the research, the drafting analysis, and review all running simultaneously. So a slow agent is a tool that you wait on, and I don't think that's necessarily a bad thing because what I have always done when I'm waiting on agents, I'm usually reading the chain of thought. Right? If in in in that five minutes, I'm literally looking at the chain of thought. I'm making sure that it's calming the right tools. I'm making sure it's pulling in all the correct dynamic data because it's like, what else am I gonna do? Well, yeah, probably multitask with some, some different agents and go in and check on my blue lights that, you know, require my input.
Jordan Wilson [00:16:04]:
But, ultimately, I'm spending more time do being the expert driven loop. Right? So I think that this is actually could be a big problem. Right? When AI is almost too fast, even the people that are maybe trying to be responsible, right, all of a sudden, everyone knows. You know, in a year or so, everyone's gonna know, oh, that AI is gonna be way more powerful, and it's gonna be even faster. Whereas right now, if you're using AI in the right way, in theory, right, you're getting more done, but in you, it's almost like you're going slower, but way faster. You know, it's like you're waiting, but you're accomplishing more. Right? So instead of working on how we would traditionally do it, you know, in pre AI days, you know, me, one human, I would work on one project across these 20 little steps. Now, conversely, I'm working on 20 little projects, and my only one step is just checking in on what the agents are doing.
Jordan Wilson [00:17:01]:
Right? But, you know, usually, I stagger it in a way where I can, understand, kind of, you know, prioritize, explainability and and, you know, making sure the guardrails that I've set are being followed, making sure my agents are crashing, all of these things. But what happens when this is done? Am I still gonna go and do that? I hope so. But when things are done faster and when the models are smarter, won't my human nature just be to be like, well, I should go grow the business in a new way. I should go get more and more things done. Right, a fast just team just now needs more management. So directing agents is just middle management without the manager title. Right? I do see the future, where most, you you know, most knowledge, you you know, subject matter experts, most people, if if you're a knowledge worker, you sit in front of a computer, and you create value for your business, which is what many of us do. I think people, for the most part, you're gonna be like a middle manager, right, of just agents.
Jordan Wilson [00:18:06]:
I like I I actually think that, in theory, the middle management layer is gonna get bigger and thicker. I think there's gonna be fewer people at the entry level. I think there's gonna be fewer people in the, you you know, the VP, C suite director level. I think for the most part, you know, your average knowledge worker, that middle, middle management tier, right, when we think it's not really needed, well, I think, you know, if we look at the middle management today in an AI native world, it's not needed. But I think just everyone else is gonna become kind of that middle layer. Right? People are gonna be managing more agents, not people. And, yes, there's still gonna be, you know, humans reporting up the food chain. But I think for the most part, the overwhelming majority of work is actually gonna get done in that middle management layer, where traditionally, that's not where the work gets done.
Jordan Wilson [00:18:53]:
Right? They're just managing people. So it's I I think that's ultimately what's gonna happen, but I think it's normal in AI native companies for decision makers to just publish an agent to help to speed up productivity. Right? You've seen these, you know, stories where literally, you you know, a a CEO or a VP or, you know, someone that manages a big department at a huge company is just literally unloading their entire brain and decision making process and all their documents and all of these things into an agent. Right? Let's say it's it's, you know, marketing Jane at a Fortune 500 company. You know, Jane just, you know, dumps her brain, her decision making process, everything into an agent, and the, you know, 500 people in marketing, well, they can just get answers straight from the Jane agent. They don't necessarily have to wait. So the work, it just becomes faster, but it becomes four verbs, I think. You decide what matters, you delegate, you inspect the exceptions, and then you integrate the results.
Jordan Wilson [00:19:49]:
Right? So, but those things can happen now like agents can in parallel, and you have to push that pattern to its limit. And that one person just directs more hours than exist. Right? That's that's where I find myself, and I found myself, you know, since, kind of, Claude Cowork, and Claude Code kind of kicked off this phase in late twenty twenty five, early twenty twenty six, and I think codex, has really took in, taken over the narrative ever since. But, you know, essentially, I'm directing more, hours than exist in a day. Right? I'm literally getting done what used to take me even two years ago. Right? Even in the early Chachibuty days, I'm getting, about thirty hours of twenty twenty four me work done every single day. So it's just you're directing more hours, more traffic than can fit on a traditional runway. And, you know, in, OpenAI report came out with this, they showed how their team was using codex.
Jordan Wilson [00:20:46]:
And, what I found interesting was, OpenAI staff for ninety ninth percentile generated over sixty hours of agent turns every single day. So, yeah, that's kind of the anomaly. Right? That's not the average because they're building this technology. But I think eventually that is gonna become the default, and that is gonna force people, I think, to question, how do you work? Right? What is the value of my actual time and how can we responsibly, you know, use these agents as they get even smarter and even faster? I actually think we're in a nice little groove where we are today. Right? There's been all this, you know, talk, in the last, you know, thirty six hours or so of this concept of AI pacing. You know? Essentially, a lot of researchers, particularly at OpenAI, Anthropic, but also Google, Meta, and other labs, you know, kind of sign this letter saying, hey. We support, you know, essentially calling it, like, pacing. Right? But it's kind of just means, like, hey.
Jordan Wilson [00:21:44]:
It's okay if we slow down. Will that happen? Probably not. Right? Is it the responsible thing for, you know, some sort of AI pacing to happen? Probably, because the issue is the capability gap is growing. The model overhang is growing, and it's not gonna get any slower. Right? Especially as, you know, over the past couple of months, we've gotten, you know, real results, from the big labs talking about RSI, recursive self improvement, where the models are improving themselves. Right? Yesterday, OpenAI literally just announced, like, an 18 improvement via RSI. They essentially said, hey. We got you know, we use g p 5.6 to make itself better, and it's, like, 18% better.
Jordan Wilson [00:22:24]:
It's gonna see, you know, 18% cost savings now. So this is where it's at. These models are gonna get better and better, faster and faster, but way at a much faster accelerated rate than humans can keep up with. So, yeah, there's this whole pacing thing that maybe we'll tackle at a different time. But the the the the brutal reality is if you want your team to be able to keep up and get ahead, that's something that you have to tackle right now head on. Some other good stats that recently came out. A Workday Foundation study in May found that 86% of users felt more productive. But those same workers describe the conversations, that rarely moved past transactional work needs.
Jordan Wilson [00:23:05]:
Right? And that's kinda where I feel right now. It's like, yeah. I'm getting way more done. The the content I hope is good. The quality is good. Right? What I produce is much different than what most people produce. But it's just kind of transactional. Right? I've talked many times on the show about I'm not even because my, you know, the throughput, the output is so insanely high for what I'm doing on a daily basis as an individual full time human.
Jordan Wilson [00:23:31]:
I don't retain information like I used to because I think so much information now is transactional. And and maybe that's something that we, you know, the future workforce needs to understand, because I think, you know, have you ever thought about how you can learn a concept better if you write it down by hand or if you teach it to someone? Right? I'm a big believer in you haven't really learned something until you teach. Right? That's one of the reasons why I started a daily podcast. I'm like, I really wanna learn this AI thing. I need to learn it well enough that I can teach at least one person. Right? Luckily, hopefully, it's helped more than one person. But I think the same is true. Because AI, it it it does all that hard work for you.
Jordan Wilson [00:24:11]:
Right? You no longer have to go on to 20 web pages and read it all and be like, this doesn't make sense. This does make sense. I'm not sure about this. Let me look at a little more. Now you just put something into whatever AI system you're using. You get a personalized, customized output, and, well, most people don't even take the time to truly read it and understand it. So, you you know, there is this, disconnection, between, you know, the the productivity and disconnection that's shared in one workday, and this study shows that that is the mechanism. And I've actually, you know, touched on some of these other concepts.
Jordan Wilson [00:24:46]:
So, you know, if if you do wanna, you you know, dive in a little bit deeper on, you know, some of these, downsides of using AI the right way, I actually had an episode, episode seven fifty seven, that was part of the start here series. It was called the seven silent sins of doing AI right, how to spot and overcome the invisible AI work traps. And three that I wanted to share that are kinda relevant to what we're talking about today is number one, accidental deskilling, which is kind of one of the things I just referenced there. That's where AI does the work, so you lose the skill. I talked about the agent bun sandwich, and that's when agents start to hollow out your core expertise, and then the compression tax where the AI speed overloads your brain. And that's where I think we might be getting into, right, with this, you know, this cerebras, right. Which might sound like a dorky niche thing. And you're like, okay, why do you keep talking about it? Because again, think if you are an AI power user now, and I said, Hey, tomorrow you or your company, your team can accomplish 20 X what you accomplished today.
Jordan Wilson [00:25:50]:
If you are an agent pill team and I'm like, you can accomplish 20 times more today. That's some compression tax happening right there. All right. So, yeah. Make sure, go check out episode seven fifty seven. So let's be fair to AI though. Some of this stuff, we don't need it. Right.
Jordan Wilson [00:26:06]:
How many times have you gone through a three hour meeting and you're like, that could have been a single bullet point That could have been a single Slack message. Right? Or waiting two days for a basic answer is not, you know, culture or mentorship. It's just maybe the signs of an antiquated business hierarchy that shouldn't have existed in the first place. Right? I think most companies, didn't quickly redesign how they worked, for an AI native workplace. So, you know, you get to then decide, which collaboration is intentional. You you know, human to human, you get to choose, kind of pick your spots where that happens if you are AI native. Okay. So as we wrap up here, I wanna try to answer this question.
Jordan Wilson [00:26:51]:
How do you strike the balance then of, you know, the reality of we're gonna be managing more agents, which might lead to less human connection and probably an increased expectation of productivity. How do all of those three things, build together? And I know this isn't for a 100% of organizations. Right? I I understand that. But I do think that more organizations, more teams will be shifting toward this than the opposite. So how do you meet that head on? Well, I think that winning companies are going to intentionally automate the manual work, but keep in prioritize human judgment. So I think generic human in the loop will always fail. So you have to name one expert owner. I always talk about expert driven loops and expert driven loops actually require instead of one passive human generalist, it requires active expert human collaboration and communication.
Jordan Wilson [00:27:44]:
So I think that's one thing right there, but also I think winning teams, know which friction to remove and which human handoffs to protect and maybe elevate. So then how do we start to balance that? Well, I think, yeah, as, as my slides are, are losing, losing my place here. Sorry. Yeah. All, all, all screwed up. There we go. My slides got all out of order. So, here's the three human handoffs that I think you need to protect.
Jordan Wilson [00:28:16]:
So number one, you need to protect the judgment by naming the human who owns consequences when the answer is wrong. Okay? Number two, you need to protect learning by making people, explain decisions instead of blindly approving agent output. So that's kind of the difference between, like I said, passive, lazy, human in the loop, which will always fail, and the correct way to do it, which is expert driven loops, which is experts, communicating, with each other, but also expecting, inspecting agent outputs. And then last, you need to protect your belonging. Right? I think so much of this, so much of what we do in our work, it's about having this sense of belonging. It's about, you know, some people for better or worse wrap their, you know, self in, their own meaning is who they are in their job. Right? But I think you can still protect your belonging by giving coaching, recognition, and unstructured conversation a scheduled place to happen. Alright.
Jordan Wilson [00:29:15]:
So what's my big takeaway here as we get faster agents that are smarter, and maybe we're just managing dozens or hundreds of agents and talking to our human coworkers less, but producing more. Well, I think three big takeaways for me. Number one, everyone needs to increase their ambition. Right? Not just saying more prompts, more outputs, but being more intentional with what you can, accomplish. Right? Maybe not just doing 20 x of what you were doing yesterday because you can do 20 x today. Maybe instead, it's thinking higher. It's thinking harder. It's just being more ambitious.
Jordan Wilson [00:29:51]:
Number two, you always have to unmarry your domain expertise. That is the, you know, the building blocks of what I've been talking about now for three and a half years, unlearning. You have to unmarry your domain expertise because it doesn't really matter necessarily anymore. Your domain expertise is really just about that, you know, that human agent bun sandwich. Right? It's it's making sure that you give it the right context and check it in the right way, but you're not gonna be doing the 95% in the middle anymore. So you have to unmarry your domain expertise as agents get faster and smarter. And then last but not least, you have to be prepared to rebuild monthly instead of yearly. I think in the pre agentic phase of 2024 or early twenty twenty five, you know, I was still recommending that people revisit their AI strategy roadmaps quarterly.
Jordan Wilson [00:30:39]:
But I would say even if you did it yearly, you know, in 2024, you weren't gonna be left behind. Now you will. You have to be prepared to literally, unlearn and rebuild how your company operates every single month, which sounds like an absolutely asinine concept. Because before, you only had to do it every five years. Right? You know, or maybe every ten years. And then, you you know, as the Internet and, you know, web two and social media and all these other things, started to gain in popularity and how people make decisions and all that, they you know, advertising, marketing, communications, you know, those things changes. And so, you know, with digital transformation, I think that shortened, that lifespan of, you you know, how your company works, kind of your, yeah, your company's IP, not just your SOPs, but literally how your company works and runs and thinks and makes decisions. You can't do it anymore.
Jordan Wilson [00:31:32]:
As crazy as it sounds, I think probably a majority of the time for, successful companies moving forward are gonna spend as much time, unlearning and rebuilding processes as they are actually doing work the old fashioned way. Alright. So I hope this episode was helpful. Just more of a kind of a zoomed out look of saying, I don't necessarily know what the heck is going on or what the heck is happening next. But if you look at the writing on the wall, you have to see and you have to understand. Agents are getting smarter. Agents are getting faster. And we, as people who are AI leaders in our respective organizations, well, we're gonna be working with way more agents and probably fewer humans in many cases.
Jordan Wilson [00:32:17]:
So I hope this episode was helpful. If so, let me know. Please subscribe to the podcast. If you wanna hear more or less of shows like this, do let me know by signing up for the newsletter at youreverydayai.com. So thank you for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks, y'all.