Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 712: AI Agent Crash, Software Collapses and Non-Human Economies. 2026 AI Predictions and Roadmap Series: Vol 1 of 2

Episode Date: February 12, 2026

AI developments legit change hourly.That means what business leaders can do changes daily. And drastically.As soon as you FINALLY learn a new AI technique, it’s often already outdated, and approvals to use it at work can take forevvvvver.Solution: know what’s coming.That’s why you have me.I’m not a mind reader, but I spend almost all day every day talking with the people building AI, using it myself, and teaching others. It’s literally my job.Each year I publish predictions and a roadmap because you don’t have 10 hours a day to keep up.(I do.)So, today is part 1 of one of our most important shows of the year: Our AI Predictions and Roadmap series. AI Agent Crash, Software Collapses and Non-Human Economies. 2026 AI Prediction and Roadmap Series. Vol 1Let’s get it.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:2026 AI Lab-University Data PartnershipsVenture Capital Firms Shift to Venture StudiosEnterprise Demand for Grounded-Only AI ModelsGoogle AI Winning Short-Term, Building Long-TermInternal Agent-to-Agent Enterprise EconomiesSoftware Stocks and ETFs Face 2026 DrawdownMessaging as Universal AI Agent InterfaceMajor 2026 National AI Agent Crash PredictedFortune 500 Shadow AI Data BreachesTimestamps:00:00 "AI Trends and Roadmap Insights"04:39 2026 AI Roadmap Series09:28 "AI Impact on U.S. Colleges"12:11 "AI Strategy and Training Services"16:36 "Future of Venture and Software"18:45 "Three Sources for AI Models"21:56 "Google's Multimodal Model Dominance"24:15 "Google's Robotics & AI Advances"27:17 Agent Cost Attribution Systems32:56 "Messaging as AI Communication Interface"36:42 "Autonomy Crisis and Media Impact"38:13 "AI Surge and 2026 Crash"41:26 Choosing the Right AI Tools44:27 "AI Governance and Accountability Essentials"49:36 AI Disruption and Safety Warning52:27 AI Automates Competitive Analysis Tasks53:55 "AI Drives 24/7 Agent Ops"58:11Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. AI developments change by the hour, which means what we as business leaders are capable of is changing daily.

And that's both extremely exciting and really hard to grasp because as soon as you learn a new AI technique or model, it's already outdated. And then it's going to take months to get this simplest approval to use that thing at your company. So what's the solution? Well, you have to know what's coming ahead. And that's what you have me for. Granted, I'm not a mind reader, but I do spend almost all day, every day, talking with the people who are building the technology and using it for myself and teaching others how to use it is quite literally my job.

That's why each year I do my predictions and roadmap series, because you don't have 10 hours a day to keep up. I do. So I can tell you what's ahead around the curve because I'm usually in the first wave of cars and though I happen to talk to the person who's actually paving the road. Get it? It's an analogy. But that's also what we're kicking off here in what I think is one of our most important shows of the year, the 26 AI predictions and roadmap series, Volume 1 of 2. All right, let's get after it. If you knew here, well, great show to tune in. to for the first time. My name is Jordan Wilson and welcome to Everyday AI. This is your daily

live stream podcast and free daily newsletter helping everyday business leaders like you and me keep up with everything that's happening in the world of AI, how to make sense of it to grow our companies and our careers. So starts here, unedited, unscripted, live stream podcasts. But if you want to be the smartest person in AI, our website is where you do that, your everyday AI.com. Each day we put out a free daily newsletter, recapping, not only that live stream podcast. podcasts, but also everything else happening in the world of AI. So a little bit. Yeah, if you don't really know what this thing is, well, I have talked to hundreds of the smartest people in AI. That's what I do all year. Right. And I have this running series of notes, right? Because there's a lot of

conversations, both with guests and a lot of other people that I'm lucky enough to get to talk to. I always have great ideas. And I'm, you know, constantly connecting these dots. And, you know, I realized pretty quickly after doing this thing for like a year, I'm like, wait, I can sometimes understand what's coming before a lot of other people do because this is all I do. Right. So I've realized over some time that it's probably helpful for you all for me to share those internal notes, the off the record conversations and, you know, me stealing some insights from

some great enterprise leader. So if you don't have hundreds of hours to go through and listen to, you know, the past year or so a podcast, that's why you got to listen in today. Also, as an FYI, I don't do these things and uncheck them. I think this is our third year doing it. So I always do a kind of a grading report at the end of the year. So make sure to go listen to episodes 674 and 676. So you can kind of go audit my predictions from last year.

So I just did that audit about a month ago. So still pretty relevant. And I think it's a great kind of one tube combination between that one and this one, the 2026 AI roadmap and prediction series. And you know what? I know what you're thinking. You're like, all right,

Jordan, it's, you know, mid-February. What are you doing this? Like, why aren't you doing it in January like everyone else? Well,

number one, I don't know if you can tell. I'm still sick. I've been sick like five out of the last eight weeks. That's the first thing. But the second thing is I kind of felt and sent something in December. Actually, like late November,

I'm like, I think we're coming up for a seismic shift here. And I think that shift did happen in December and January. So if I'm being honest, my notes in December, you know, anyone's notes in December to where we are today completely off, right? Everything has changed. Everything has changed. So, you know, a couple of reasons, multifactorial. But regardless, here we are.

And, you know, thank you, everyone for your patience. I've been getting a lot of messages now almost every day. you know, people asking on, you know, LinkedIn, Twitter, uh, tax messages, you know, emails. Like when are, when is the, you know, 2026 AI prediction and Romat series coming? So it's here. So today and tomorrow two part series, not going to do five parts. Don't, don't have the time to dedicate that many shows right now to tell you guys the truth. Because a lot of our, you know, weekly programming has, you know, gotten pretty popular. And people, you know, are used to seeing the, you know, AI news on Monday and the,

you know, AI at work on Wednesday. So we're just doing two this time. All right. And FYI, a couple of their housekeeping things. You're going to want to repost this one. Just letting you know. So if you are listening on the podcast, check your show notes. We always leave the link to this LinkedIn live stream.

So go repost this on LinkedIn. And I'm going to send you our exclusive bonus guide because I actually, my first list of prediction in roadmaps was like 150. And then I narrowed it down to 50 that I actually built out pretty in depth. So obviously we're only going to 26. It is the year 2026. So we're going over the 26 top predictions. So 13 today, 13 tomorrow.

But we have a guide. It is amazingly in depth. Like I said, not even going to have time to share all of this. I wish I did. So go repost this show. I'm going to go ahead and send it to you. It's ready to go. Copy and paste.

It's on your way. All right. So I'm not going to make you wait anymore. I'm going to read off the 13 we're going over today. And then I'm going to go on each one just for about, you know, two or three minutes or so. All right. So, and these aren't in any particular order, FYI. Sometimes I do them in a particular order.

You know, this this year just kind of, you know, spaghetti on the wall. So first, number one, an AI lab pays universities for. student data, maybe an aquahire. Number two, venture capital firms morph into venture studio firms. Three, grounded only modes or models become enterprise hot items. Four, Google wins the short game while building the long game. Five, internal agent to agent economies take hold. Six, software stocks and software ETFs suffer a sustained 2026 drawdown. Seven, Massaging becomes the universal agent remote control interface. Eight, agent crash becomes a national headline events.

Nine, shadow AI triggers a Fortune 500 data breach. Ten, agent audit logs become mandatory in regulated sectors. 11, skill marketplaces become the largest agent risk surface. 12-hour autonomous task horizon becomes the new Bragg metric. and 13 agent ops become a formal enterprise function. There you go. So you have them all. Let's dive into them a little bit one by one.

So first, in AI Lab, I'm not going to say acquires a university because that sounds crazy even for me, although I do think that's essentially going to hire happen by the end of the decade. But I think in 2026, at least one frontier AI lab will, announce a structured partnership with a university centered explicitly on access to student generated data. Human data. Yeah, we need it. And I'm going to be talking a little bit more tomorrow, kind of on work slop on why this is really needed. But I think this announcement is going to be framed as more of like a research collaboration. But the real asset is big AI labs, they need high quality human generated data because it is at a premium.

And you might be wondering why, right? AI is not new. AI was around well before Chad GBT, even generated AI that was just generating text, right? The GBT models started littering the, uh, the internet with poorly written, uh, copy in early 2020. Um, so what we've seen is this, uh, uh, this cycle of slop that gets regurgitated, you know, and then future models have such a high, you know, it's, it's poisoned, right? And, you know, that's why, you know, there's all these

conversations about scaling laws and all of these things and high quality data. But it is going to happen. There's no way around it. It's like the perfect storm because at least here in the U.S., well for a couple of reasons but number one u.s colleges and universities didn't know how to handle AI so they wrote it off and they banned it right for two to three years many of them which means aside from the enrollment cliff that was already coming you know universities are already at a you know all time low in terms of funding see of that that was naturally happening the enrollment cliff and now job placement rates have gone down not just because of AI and it's getting harder for entry-level jobs, but also because the students coming out of most colleges are grossly

unprepared because all employers want students who have experience in AI and know AI and learn AI and well, colleges and universities banned it. So I think universities are going to go bankrupt. They're going to go out of business. Name brand ones, right? Quote unquote, name brand ones, ones that we've all heard of. They're going to go under. Not all of them, but I think many of them in five to 10 years. So I think what's going to happen is some of the more innovative universities are going to strike partnerships with big AI labs. I'm talking about the Microsofts, the open AIs, the Anthropics, the Googles. You know, there's probably two or three others on that list. You know, Amazon, maybe XAI will see. But it's going to happen. And here's what's,

Here's how it's going to work. Students and teachers, right? The value of what is created in the college classroom is the cognitive process, right? It's not the answers, right? It's not the, you know, that PhD paper that a student turns in. It's the red ink. It's the thinking. It's the conversations.

It's the going back and forth. And I think that even though on the surface, these things are going to be, you know, these partnerships are going to come off as, you know, oh, this is a research collaboration. All that is, it's for thinking, reasoning, data. It is to collect high quality. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant now live in the Adobe Firefly app, the all-in-one creative AI studio. powered by Adobe's creative agent, Firefly AI assistant lets you start with your vision,

just describe what you want, and shape the outcome as it takes form with the assistant. The assistant orchestrates multi-step workflows, drawing on 60 plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator, Premiere, Lightroom Express, and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks, like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible,

so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adopi.com. Human generated insights and thinking because future models, they need reasoning data. And I think that unfortunately, right, you're not going to get that from the enterprise. I think where you're going to get that is, you know, in the doctoral students and just,

you know, in general, you know, different specialized programs. So this is actually like the data scarcity is increasingly cited as a competitive constraint for major frontier labs. One of them, if they haven't already, they're going to figure this out. I'm sure it's already been figured out. I'm sure it's already been talking about. We are going to see this.

And a lot of people are going to be shocked. But here's, it's the reality. College is too expensive. So I think the students that participate are going to get compensated, right? Whether it's free tuition, reduced tuition, whatever. AI labs have money to burn. Right. They are literally sitting on a stack of money so high.

They have to duck to fit into, to think. Right. That's how much money they have. and they have to spend it somewhere. And colleges are going to go broke. Perfect storm. It's going to happen. Number two, venture capital firms are going to morph into venture studios.

What the heck does that mean? Well, if you're not in the VC area, a lot of things are happening, right? Number one, I think a lot of VCs got burned, you know, in the early chat GPT days because they invested, you know, millions or hundreds of millions of dollars of anything that had the word AI in it. Now they're getting a little bit smarter, but what's happened, right, traditionally, I would say, in the last 10 years, for most startups, right? A lot of them are, you know, SaaS adjacent or software adjacent. So think of, you know, obviously the, from 20 years ago, you know, you had the Facebooks and the Instagrams and all those.

But, you know, think of the last, you know, 10 years, 10, 15 years, you know, Uber's, Airbnb, right, all those type of things, well, they're software. So most startups need two things. They need distribution and they need engineering. Okay. Technically, venture capital firms have distribution, right? What's happened with engineering? Over the past two months, right, you've had,

I haven't even talked about this one on the show because it happened very recently. One of the head engineers at OpenAI admitted that a lot of the new features that went into Codex and some other internal tools that Open AI is using 100% written by the models. Claude Code, the creator of Claude Code said the same thing. Now everything, every update is written by Claude Code, 0% humans. So the engineering cost is going down to pennies on the dollar. So what is a venture capital firm to do? They get pitched these same types of ideas over and over and over. And they're like, unless it's an AI lab, I get it.

I'm not saying that. I'm saying for anything, for anything, whoa, slipped out on my chair there. For anything software adjacent, VC firms have the distribution. And the cost of engineering has gone down to next to nothing. So why wouldn't a venture capital firm turn into a venture capital firm turn into a venture studio, right? Have their own in-house engineers, right? And, you know, your startup, you know, you get to come work with us. You know, we help you. We have all the processes down. Cuts your engineering cost by 97%. Your go-to-market is 3x, 4-X faster. And they have the distribution

right there. And then instead of getting, you know, I don't know, 5 to 10%, the VC funds are getting, you know, 30% you know because they're venture studio. If I'm being honest, I think a lot, it might not happen until 2027, 2028. But if I'm in the VCC right now and I'm seeing, but they're not seeing what I'm seeing yet, right? Go talk. Go talk to the people building the tools like I have and you'll start to see it. It is a crazy weird world in terms of what's possible with software right now.

And, you know, whether you realize it. or not, it actually has a huge impact, not just on the tools that we all use on a day-to-day basis, right? If you're a knowledge worker, you sit in front of a computer and you use these tools that, you know, maybe they have been around for 20 years or for two years or whatever, but they're going to constantly change. And almost everything starts with venture capital, private equity, venture studios, et cetera.

So I think that's going to change drastically. All right. Number three. excuse me all right grounded only models become an enterprise hot item

so what the heck is that well here's the actual prediction all right so by the end of 2026 at least one major enterprise

you know AI lab will ship a grounded only mode here's what that means notebook LM right notebook LM for the most part

doesn't have any major competition. And maybe it's more from a marketing perspective, because I do know that there's certain ways that you can accomplish this, you know, in different tools, right? I know that Microsoft co-pilot has had something like this, but it's almost like a hidden feature that no one knows how to use. So I do think that, you know, one of the big labs is going to lead with this. Technically, Google already has it via notebook LM, right? But grounded is huge. So what this means is there's essentially, you know, three different sources that a large language model will draw data from.

You know, first and foremost, it's looking at its training data. And then depending on your setup, depending on your custom instructions, what model you're using, et cetera, it's either going to go to the web or it's going to connect to your personal data. Those are the three sources. But for so many things, right? You don't want training data and you don't want the internet in many cases, right? You just want your company's data, especially if your company is a large enterprise and you have a ton of data, right? So I do think that specifically either Open AI or Anthropics is going to release this because like I said, technically Google already has Google in Gemini, They already have access to notebook LM.

So there's kind of ways to already have this. But either Microsoft is going to lead with this and put some marketing around it or OpenAI or Anthropic. Someone's going to do it. Notebook LM continues to just meteorically rise, right? It is still on fire in terms of the millions and millions of people who are using it weekly. It's growing at a rate in terms of percentages that is faster than the other labs, right? Nobook LM, if it was its own lab, it's been growing the most, you know, month over month compared to everyone else. Because consumers and enterprises want this.

They want the option to have that grounded only. I've always said, like, I know it's harder than this, but there just needs to be a grounded button, right? Use my data only, nothing else. There's certain ways you can do this, right? like Open AIs new, you know, they just released an updated version of their deep research that you can just do like certain websites. So that's kind of a version of that, right? Like if your company is, you know, big company and you have four or five different, you know, entities or products or companies under your umbrella. So, okay, there's a way that you can kind of do it.

But I think this is going to be a de facto mode pretty soon. And also just with the long context windows as well. All right. Our next one, number four, Google is going to win the short game while building the lawn game. So here's this one's a little harder to measure and maybe this is more taste or judgment. But I do think that Google is going to at any point just come out and win every single benchmark again and again and again. So here's what's happening. Right.

Google Gemini 3 came out in November. Okay. So now the release cycles are silly. They're so fast. So now, depending on what you look at, but for the most part, you know, whether you're looking at arena, formerly LM Arena, or any third-party benchmarking sites, or, you know, just the scientific benchmarks themselves, you know, in November, Google with Gemini 3 Pro was untouchable, every category. It is still, I'd say the best multi-modal model by far, right? Just because it's the only multimodal model by default that can ingest video, right, all those other things. But a lot of benchmarks now, Google's been out of it.

But at any point, at any point, Google more so than Anthropic or Open AI, right? I think that Anthropic and Open AI, when they come out with a model, it's going to do well in certain benchmarks or certain, you know, kind of arena rankings. When Google comes out, it's going to win every single one, every single time. And I know that Google, whether they're, you know, going to come out with their Gemini 3 Pro GA, so they're generally available version or if they go to a 3-1 or a 3-5, whatever, at any minute they can snap their fingers and it's going to be done. Google is at that point now when they release a model.

it is undoubtedly going to wipe the floor. But I think, yes, they're going to win the short game, but they've been building the long game silently. And here's what I mean by that. Right. So for the most part, I don't know the exact percentages because it's all proprietary information. All the weights and the training data is technically private. Not a lot of people know about it.

But I would guess that at least every single lab is using about at least 90% of the same training data. So it's like, okay, that's why you pay. pay your engineers tens of millions of dollars a year. You know, that's why you're entering into these exclusive, you know, partnerships with certain news publishers to get exclusive access to that for training data, right? But Google has always had something that no one else has had that is a gold mine, especially as models get smarter.

And that is YouTube, right? You can make the argument like, oh, you know, X has GROC. okay, Grock has been shown year after year, study after study to be the most disinformation, heavy social network out there, right? Google has YouTube, which has an ungodly amount of hours of human video. And I think the long run, right, the long run has always been about embodied AI, right? And what helps with that? Not just text, video, the understanding of the real world.

I think that's why we've seen things like, from Google, some of their, you know, their robotics models are really good, came out of nowhere. You know, and now in instance, you know, tier S or, you know, first tier competitor on the robotics model side. Right up there with, you know, what Nvidia is putting out. And then you had their, their Jeannie 3 world model. Absolutely nutty what that thing can do. It can, it looks like a very realistic video game that's rendered in, real time, right? So Google is playing the long game and they have been playing the long game for a

very long time. I think even in, you know, 20, 23 when I started the show, I wasn't privy to that information. You know, now I know. Google is playing the long game. And they've been, it's been quote unquote, long enough that they've been able to close the gap on the short game. But I still think that they're going to win because in the end, it is about embodied AI. And Gemini, you know, is consistently ranking at the top and they're going to come back and do it as well. All right. Number five, agent to agent economies. That's going to be a thing.

All right, here's the actual prediction. In 2026, enterprises will deploy internal marketplaces where agents can dynamically delegate and hire other agents. Here's what I mean. So, essentially, software that companies would normally make, right, and how you pay a a monthly fee for a piece of software. But then you, the human, still have to go do it or your team still has to use a piece of software. What I think is going to happen is agents are going to hire other agents.

So let's just say I work for, you know, Big Corp A, all right? In Big Corp A, we have been using, you know, Software A for 20 years. and it's very expensive. But now Software A has an agent marketplace, and it's more output-driven. And my Big Corp A, we've been ahead of the game on AI. We've been using multi-agentic orchestration since late 2024.

And our agents are really, you know, we have more agents than human. So I know that sounds weird, but a lot of companies are in that position. And those companies that are, this is what's going to happen. You are literally going to have. enterprise agents out there. They're going to have budgets. And they're going to go to, you know, big software A that's now going to have an output priced agent.

And you're just going to have agent to agent commerce, but doing tasks that humans are going to do. And the humans at big software A aren't going to know that we're getting our job done. We will, but we're not going to really always understand how, right? So this is going to like literally finance departments are going to require cost attribution per agent run. Right. So you're going to have all these complex tasks that normally, you know, groups of teams do.

They're essentially going to be, you know, decomposed into these subtasks that are then going to be executed by specialized agents. But it's going to be agents that are going to be negotiating with other agents because now we have these different protocols. I can't even list them all, right? But there's now a handful, right? One of the most popular ones is MCP, but there's A2A, agent to agents. from Google. Right now, agents have languages that they can talk to each other, whereas, you know,

a year and a half ago, they didn't. But now they also have their own commerce protocol as well, right? I know that sounds weird to think and like, oh, no, that's, you know, 2050. No, that's technically what's happening at the end of 2025. But that is what is going to happen in 2026. You are going to see on the enterprise side, agent to agent economies. All right. Number six, software stocks, got to go down the, down the tube, down the tube, sorry. I would especially look at ETFs, right? So these funds, right, these electronically traded funds that are groups of traditional software companies. It's already started to happen. We've seen some of these, you know, ETFs already go down like 20 plus percent year to date, which is, again bonkers right if you don't cover you know if you don't pay close attention to the stock

market or ets for something to be down 24% year to date it's bad and it's going to continue to get worse for those companies and one of the reasons why right and i'm not saying i'm not saying that you know someone's going to go vibe code you know the next uh you know docu sign or something like that but those type of companies um public companies um public companies huge enterprise companies, they have tech debt, right? They can't just, you know, snap their fingers tomorrow and become an AI native company. They have tech debt. So they either have to start over or they have to spend months or quarters just to sometimes

implement features that startups can implement in hours, right? And a lot of times it will work and function better. right um i like to think of it like houses right if there's a house but let's say software is more like dog years or 10 to be easy right so a piece of legacy software that's been around for you know 15 years that's like a 150 year old house right good luck in the pipes on that one even if it's a great house right a house now that's built the right way even though some stuff is built cheap now right you know, old man Jordan on my porch. No, but you'd rather have the new house.

Always. Always. Because everything is changing, right? The foundation, the type of concrete, right? Oh, now these bricks get moldy, whatever. Right. The same thing can be said with, well, enterprise software. And it's going to get gobbled up. Like I said, I think the winners are going to be those that own the data and the distribution. And the losers are those that just sell expensive UIs that agents can bypass.

That's the other thing. I think we're going to see in 2026 that a lot of the software, I won't say a lot. I think we're going to start to realize that even just software in general is going to be less needed because it's going to be agents interacting with each other. Again, right? If you're able to set up guardrails and have expert-driven loops of multi-agent or multi-agent orchestration,

software becomes less and less needed. It becomes outcomes-based, quantifiable, traceable, observable. When those things are happening, it's the process or the journey doesn't necessarily matter anymore. Right?

We've just become so accustomed to, oh, we the human, use this piece of software to get this outcome. But we, the human, okay, well, now there's these models that are smarter. than us. Software. Well, what's the point? If agents can talk to each other and bypass the software,

why is the software even needed? That's what we're seeing. And I think the most essential, you know, I'd hate to throw out like docu side, but you know, that's a great example, right? They're, you know, a huge company and expensive. And it's kind of like, you know, Comcast Xfinity, right? It's one of those companies that everyone complains about. It's like, oh, this is the same. simplest piece of software, you know, you go in and sign something, you know, but companies are paying, I don't know, thousands of dollars a month, you know, to use it. And everyone's like, wait, why? Right. Things like that are going to be extremely, extremely and quickly, I think, disrupted. All right. And related to that, number seven, messaging becomes the universal control.

I think one of the reasons agents, I'll say general agents, because I think narrow agents, did really, really well in 2025. But I think one of the reasons that general agents didn't take off like we thought they would in 2025 is, well, the interface. You have to rework your daily processes. I think what's going to happen is how we talk as humans day to day. That's going to become the interface. Specifically, I'm talking about messaging, Slack phone calls.

I think that's how we're ultimately going to be communicating with agents in the same way that we would communicate with other humans. How do you talk with humans? Right? If you're not face-to-face, how do you talk to your coworkers, to your family? You text them, right? Your coworkers, you slack them.

Right? You know, maybe something like WhatsApp, right? Telegram, maybe, Discord. This is how we're going to be talking to agents. Because now that agents kind of, kind of have these different languages that they can speak and things easily translate. We don't have to use these clunky, you know, or not clunky, right?

But we don't have to fit our messaging and our processes into a new hole. We can just do the same thing that we've always been doing, right? Bust out your phone, text, done, right? Have your agent call you, pick it up. Yeah, go ahead and do that. Done. This is in the future. Literally what I said there, it's.

available, right? A lot of people have heard of, you know, the open claw or claw bot, right? It's a good example. You know, this is an open source agent that you can connect to a lot of your data. I think dangerous if you don't know what you're doing, but if you do know what you're doing, and as the open source project becomes more and more supported and, you know, with harder guardrails, I think it's a little more, it's a little safer, right? But how do people communicate with it? Well, text it. I message. People have, you know,

figured out to use 11 lads and their agent calls them, what it needs, you know, their, uh, call on something. But I think that's what's going to happen. Um,

I think a lot of people, don't see the value in that. And they're like, no, I'd rather sit down and, you know, click some buttons, but why,

right? Right? Why? We've been communicating more or less the same way. You know, phone call, emails, you know, since the 90s or, you know, phone call since forever, you know, emails since the 90s. And then we, you know, messaging, Slack message, text messaging for the last 20 years, right? Clearly something works about it, even if you think it's inefficient, right? It works.

And it's going to continue to work but for agents. All right. Next, there's going to be an agent crash. Not what you're thinking. But here's the prediction. So in 2026, there is going to be a high profile autonomous agent misaction incident that triggers national regulatory scrutiny. All right. So I'm not talking about hacking or a bad prompt. I'm talking about something that is going to be like, you know, when those data leaks, like the, you know, what, like the Home Depot data leak that was, you know, millions. It's going to be something that is going to grab headlines, not in the AI world.

And I don't think it's going to be, you know, an agent that hacks model ways. or anything like that, it's probably going to be as simple as prompt injection and tool permissions, right? An agent's going to go rogue, right? We already saw a small, very small micro versions of this with the, the Malt book, right? The social, the AI social media social network site for open claw bots. You know, we already saw some small examples of this, but it's going to be big. And I think the media coverage is just going to focus on kind of the autonomy gone wrong.

And I mean, we're going to see, I think the congressional hearings might not happen in 2026. But I do think it's going to get to that point. I think we are going to see a big incident. Right. This is, think of how much, you know, going back to my old journalism lingo, how much pub, right? How much pub that it got when the, you know, my, going back to my old journalism lingo, how much pub, right? How much pub that it got when the, the lawyers, I believe they were from New York.

You know, early on, they just were copying, pasting stuff from chat, GPT. They didn't look it up. You know, it hallucinated these sources, whatever. That has still been an international story for years. And that was just people, you know, copying and pasting something that wasn't true, right? This is going to be bad. This is going to be bad. Remember when I talked about those, you know, agent-to-agent communications and enterprises are going to do this,

even though the risks are high because the reward is higher. And sometimes the risk is actually when enterprises are looking at this, they're like, well, our competitors are going to do this. And if our competitors have thousands or millions of agents working around the clock, they're going to gobble us up alive if we don't match them. So I think it is this not like a fomo. It's a rush to not get lapped because now what, like I started this show saying, what individuals, teams and enterprises are capable of,

it's not an exaggeration to say that we're going to see the the smart teams it's not doubling it's not tripling i mean you're technically able to five x your output right with the same resources that you're putting in if you know what you're doing if your data is in a row if you've already trained your people on i i think in theory agile companies are going to be able to five x their outputs without doubling their inputs right keeping their inputs the same which is going to create this, this rush to go as agentic as possible, which is going to lead to, I believe, a huge and noteworthy agent crash in 2026. And it's going to lead to backlash.

And I think the backlash is actually going to be good because it will finally, you know, nothing is going to get, you know, codified, solidified from a regulatory standpoint. But I think it is the least going to get the regulatory, you know, conversation actually going, right? because regardless of what your feelings are on President Trump's stance on AI, he's made it the Wild West, which a lot of people really like, right? But I think what that's going to lead to eventually is it's going to lead for an opportunity like an agent crash. And that's what we're going to see.

All right. Number nine, very similar here. Shadow IT is going to trigger a Fortune 500 data breach. So here's the difference between what I'm calling the agent crash in prediction eight versus the shadow AI data breach in prediction nine. And number eight, the agent crash, that's going to be fully signed off on, right? This is going to be, no, we intentionally put this agent out in the wild and it went terribly wrong, right? And this is a brand new technology and it, you know, didn't go how we wanted it to, right?

Unfortunately, if you think back to the earlier days of aviation, there's a lot more plane crashes in the earlier days of aviation. it was brand new technology. It changed how people lived their lives. And there was a rush to, you know, make that technology better and to give it access to more people. So I think with number nine here with Shadow IT triggering a Fortune 500 data breach, shadow IT is unauthorized, right? When the agent crash is going to be an authorized agent that just goes off the rails or

people, enterprises didn't build the right rails. Here, I think by the end of the year, I'm calling it. It's going to be a Fortune 500. And it's going to be a public data league. And again, not to keep looking at like Moldthbook, right, the example of the, you know, clawed bot turned Maltbot turned open claw bots, their own social media network. But a research company came out and said, oh, wait, there's millions of, you know, exposed data entries here.

I think we're going to see something very similar, but from a Fortune 500 company, which is what's going to make it kind of noteworthy. And here's why. Companies aren't training their people on AI, right? That's number one. Number two, they're locking down access, right? Those people, I think for the most part, you know, enterprise companies here in the U.S., they have access to Microsoft co-pilot.

I think smart teams have been choosing their AI operating system, but for everyone else, you know, they're like, wait, I don't have co-pilot where I need it or I don't have access to this. So, all right, on my second computer, or I'm just going to open a, you know, incognito window here and just, you know, use chat GBT or whatever. Or use, you know, even worse, right? I've seen enterprise leaders, like, ask me. I get messages all the time and they're like, oh, Jordan, what do you think about this piece

of software? And I'm like, no, it's garbage. Don't use this, right? It's all these, like, people literally, in the enterprise, people are looking at, like, rappers still, right? Not like, you know, Snoop and Eminem, like rappers with W, right? Just these, uh, these pieces of, software that pop up, you know, and it's like, this could be one dude building this. And you're,

you know, thinking about moving your company over to this piece of software versus like chat GPT or Claude. Like, no, don't do that. Right. But that's what's also going to lead to a data breach is people don't know any better. Right. They're going to say, oh, this is like a chat GPT, but it's for construction. Oh, great. I'm in construction. I'm going to go ahead and paste all my stuff in here. no, you know, don't. It is going to lead to a data breach. And that is, well, this is also a multifactorial. It's training and education number one.

But then it's access, right? Companies aren't giving their people access to the right models that they need. So yes, this is going to, I think it's going to reach a point where even copy and paste might get locked down at some companies. I think it might get that bad, right? Not permanently, but I think, you know, companies might. get to that point where they're going to lock down copy and pace because of shadow IT, right? So many, you know, people here, knowledge workers in the U.S. are working remote or hybrid. Companies, you know, smart companies know, the shadow AI that's becoming a problem.

And companies, well, I think a huge Fortune 500 data breach and a lot of people are going to have a freak whiplash reaction and make some crazy, some crazy decisions based on that. All right. Number 10, agent audit logs are going to become mandatory because of, yeah, I put these ones in order for a reason. When you have something like an agent crash that's going to happen and become a national headline and you have a shadow AI that is definitely going to trigger a Fortune 500 breach, yeah, all of a sudden, especially in regulated sectors, finance, legal accounting, etc., they're going to require. audited logs of every single action an agent takes. Right. So as an example, if, oh, if an agent denies a loan now, well, you need a log of exactly why. There's no more black boxes. There's no more, you know, opakness in your AI processes.

So I think as business leaders, and this is why this is part prediction, part roadmap, right? This is going to come, especially if you work in anything that, you know, where discrimination can be determined, you know, finances, loans, health care, et cetera. And those are the industries now, obviously, you know, trying to take advantage of AI. You have to be thinking ahead. You have to say, okay, how are we going to be able to audit our laws, right? Are we able to see, do all of our agents have ID?

Who's observing? Who's, you know, going back and tracing all of this? Right. How often are our experts going in and reworking our processes? Governance all of a sudden has to become embedded in the architecture. And it's no longer just the competitive nature, right? All of a sudden, these buzzwords that we've been thrown out to sound like good smart AI people, like governance and guardrails and responsible AI, all of a sudden, those become more than buzzwords. They become lifelines for your company, right?

Otherwise, you know, I'm not going to go this far and say that there's going to be companies that are going to be shut down. Maybe that might help happen in the healthcare space, but it could definitely happen. All right. Number 11. The skill marketplace becomes the largest agent risk surface. So here's the prediction by late 2026. At least one major Asian ecosystem experiences a high profile security breach via malicious skill distribution. So yeah, there is actually a study again.

I know I keep going back to the whole clawed, sorry, open claw, right? So a researcher came out and said the most downloaded skill for OpenClaw, right? Again, this is an open source project. I think it's great. It's fantastic. I still think it's not quite safe enough for the majority of our listeners,

which is why I haven't devoted a lot of time, right? but the number one skill on kind of this skill marketplace was malware right and everyone's just rushing to do it um i do think skill marketplaces are going to take off right when you start looking at uh you know even as you start using programs like uh open a i's codex more you know which now supports the skills um as you start looking at um you know anthropics cloud code code Claude Co-work. You know, there's skill support now across, you know, some of the Google Gemini products as well. And then essentially skills are these, they're folders and markdown files, right?

So do you know how easy it is with a large language model to be like, hey, here's 10 of the top skills. Make me 10,000 more, but inject malware into all of them. And then make them all industry specific because people are going to love those. Right. And they're going to over, you know, they're going to like, oh, this is just like that popular one. But for construction, yay, I'm going to use this skill for Claude Co work. Oh, now all of a sudden, Claude Co Work, which if you don't know what you're doing and you just give it access to your entire computer and it has terminal access, it can read and write your files. And now all of a sudden, you just blindly gave it a skill that is injected with malware. It could go ahead and take every single file on your desktop and upload it to a public server.

and goodbye everything that you have on your computer, right? It is now public in the hands of someone else and you may not even know it. We are going to see something like that. I don't know how big this one is going to be, but it is going to be a huge risk. So it's kind of like downloading a virus from an app store, but it's for your corporate agent that is out there autonomously making decisions for you. That's the biggest thing, right? Think of this happens all the time, right?

I'm sure your parents are in-laws or someone, right? If they're in their 50s, 60, 70s, you can probably relate to this. Someone's had a virus on their computer, right? The difference is that person probably knows and they're like, oh, man, virus. And they turn their computer off and hopefully nothing bad happens. Right. When an autonomous agent, if you give it a skill and you don't fully know what's in that skill, that tells the agent what to do.

And most people want their agents working 24-7. And the more and more access that you give to that agent, things could go bad. Yeah. So like I said, that was Open Claw's Claw Hub, that there was already some hacks on there. But I think it's just going to be malicious skills that are disguised as productivity tools.

And they're going to silently just ruin lives, ruin departments, ruin careers, ruin companies. It's going to happen. Right. That's why this is part prediction, part roadmap, y'all. I'm telling you, you have to be aware. Like, I'm not going to go off into like things are getting weird. I always try to have a little bit of a positive, you know, spin at the end of the day, even though I do think AI is going to take more jobs than it will create. Things are going to get very weird in 2026 in terms of capabilities in terms of human purpose, right? You're going to see some AI that's going to do something that you've been doing for 30 years way better, way faster than you. And you're going to be like, what's my purpose? is here. I'm not going to get into that. But when it comes to safety of, you know, if you're a decision maker, if you're a department head, if you're an entrepreneur, you have to be safe. You really have to understand what you're doing. Don't rush in, right, to, you know, give your Claudec, all these skills, especially if it is a computer that you're giving it access to

different file folders, different structures, right? It has terminal access. You have to be extremely careful. All right. Two more. Number 12, 12-hour autonomous task horizon. So I am saying the meter metric. All right. So we talked about this a little bit on the show before. So meter is essentially, it's M-E-T-R. They're a nonprofit. And they essentially measure how long large language models can do a long work task that humans would do at a certain pass rate level. Right. And it's been growing significantly, like a year and a half ago, it was like 45 minutes. Now it's multiple hours. But I think by the end of this year, I think we're going to see at least 12 hour, right?

12 hour at that 50% success rate, maybe even 24 hours, right? So this means that an agent can successfully complete work more than 50% of the time, a task that would take a human more than 12 hours. Do you know how crazy that is? just the models that we use every day, right? I'll probably do a show on meter because there's also a like a 90% pass rate. This is a little bit different than GDP Val, which we're going to be talking about on tomorrow's show, right?

But this is the ability for a out-of-the-box model to do 12 hours of work. It's going to happen this year because it's already getting close, right? It's already up there in the, I believe, 7-8. 8-9 hour range with some of the more recent models. But I think that AI is going to move from a tool to a functional employee, right? Maybe a junior level employee in 2026, but undoubtedly, it is going to be able to long tasks, these long task horizon times and the capabilities are just going to increase. So an example, right?

I think we've been thinking of AI as giving it a task. giving it a task or a series of tasks. This is giving it hard projects, right, that would take someone a day or a couple of days or it might take a team an afternoon. So an example, right, maybe researching competitors, building a competitive analysis website scraper that scrapes your competitors,

you know, pricing page updates over the course of a year, and then it debugs the software, and then it sets up a database with everything that it scrapes, and then it writes a memo based on everything it found. right. If I were to tell you that, you probably need a small team and they probably, you know, spend a good, you know, day or three on something like that. Um, an agent can go out and do that now. Right. Go do research. Go scrape some data. Go build a tool with all that stuff that you did. Go run the tool. If it breaks, go fix it. Um, and then, you know, write me a couple briefs and,

you know, make some decisions based on that. And that's all you tell it. It's going to go out and do all those things autonomously, right, for many, many hours at a time. All right. Last but not least, number 13, agent ops. That's going to become a formal enterprise function. So here is the actual prediction by late 2026. Large enterprises are going to establish dedicated agent ops teams responsible for monitoring and maintaining AI systems.

So I actually saw some tweets about this recently. I think it was actually from Greg Brockman, president of Open AI, said something along the lines of like, not that he had FOMO, but it was almost like, if his agents weren't running 24-7, he felt like behind and felt like he's missing out.

And I think that's going to be for enterprises on the cutting edge, right? companies, huge companies that have gotten AI right, it's going to be like that. They're going to have, if they don't already, thousands or tens of thousands of agents that are running 24-7, which means a new breed of work is going to come.

Yes, AI is going to create new jobs that didn't exist. And I think a lot of that is going to be agent ops, right? I'll even get crazier. I would say by 2030, many of us are going to be in some form of agent ops. And the same way right now, knowledge workers, you know, we're in some form of, well, we all just use the internet and software and we make decisions, right? It's going to be the same thing. All of our, right, this is more in like the 2030s.

We're all going to be an agent ops, just different types. Right. We're going to be, you know, front end, back end. I call it, you know, we are becoming the buns for the agent sandwich. We give it, right, directions and context on the. front end and then feedback and kind of iterations on the back end, but the agent does the majority of the work. It is the meat, the cheese, and the condiments in the middle, right? But I think that agent uptime is going to be one of the most important KPIs for a lot of enterprises. And, you know,

drift, you got to be able to detect drift, right? Prompt changes must be version controlled, you know, incident responses must include agent behavior, right? Agent ops is going to become, as normal as DevOps and probably more commonplace in a couple of years, but in 2026, agent op teams, it's going to be a very normal thing, right? It is the full industrialization of AI, everything from audit logs and change control meetings, everything, right? You are literally going to have large teams of people that all they're doing is, you know, investigating the operations of agents and coming up with ways to optimize them

improve them and scale them. That's all they're doing. Large teams of people, just watching dozens, hundreds, thousands of agents and making sure they're operating right. That's it. That's all these teams are going to be doing.

You know, going beyond, you know, simple orchestration, this is like large DevOps, but with agent ops. All right. So that is a wrap for today's show. Let me go ahead and again,

give you the question. quick two-second run down here in case you fell asleep halfway. So number one, an AI lab essentially is going to not aquahire, but kind of aqua-hire universities for student status. Two, VC firms are going to morph into venture studios. Three, grounded-only mode is going to become a huge thing in enterprise. Four, Google is going to win the short-term game while building the long-term game. Five, internal agent-to-echant economies take hold.

Six, software stocks and software ET. CFs suffer. Seven, messaging becomes the universal agent remote control interface. Eight, agent crash going to become a national headline related to number nine. Shadow AI triggers a Fortune 500 data breach. Also related to 10, agent audit logs become a mandatory thing in regulated sectors. 11, skills marketplace become the largest agent risk surface. I think we're going to see 12 hour autonomous task horizon becoming the new metric that

companies go after. And 13, agent ops become a formal enterprise function. All right, that is a wrap for volume one. All right. So make sure you join us tomorrow for volume two of our 2026 AI predictions and roadmap series. I hope this is helpful. And like I said, go find today's show on LinkedIn. Go repost it.

You are going to want this exclusive bonus guide that we put together. I didn't even get to half of my notes for these 13. I'm handing it all over to you. This is one of those things where I'm like, yeah, I could charge money for it. No. All right. If you find any support in the show, if you found any help over the course of 2025, take, you know, 10 seconds, 20 seconds out of your day to go, you know, find that LinkedIn live stream.

Just click it if you're listening on the podcast like many of you are. Go repost this and I'm going to send this to you. All right. So thank you for that. Thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks y'all. Meet Firefly AI Assistant.

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