Ep 840: The Next 12 months of AI: 19 Predictions Every Business Leader Needs to Hear

The next 12 months of AI leaked. 

Kinda. 

For the past 90ish days, we've been quietly collecting evidence of what's next.

1,030 saved posts. 90 Podcasts. Countless conversations. Every model drop, every leak, every quiet product update the big labs hoped you'd scroll past.

Then we connected the dots.

What came out the other side: 19 calls on where AI goes over the next 12 months. And some of them are uncomfortable.

We're walking through all 19. 

Bring your team's AI roadmap. You'll want to edit it. 👇

The Next 12 months of AI: 19 Predictions Every Business Leader Needs to Hear -- An Everyday AI Chat with Jordan Wilson

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Today's Episode 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 lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:

1. Reactive Chat Dies, Proactive AI Agents Rise
2. Voice and Mobile Become AI Default Interface
3. Manager Threads Replace One-Off AI Chats
4. Multiplayer AI: Humans and Agents Collaborate
5. Company-Wide Vibe Operations with ChatGPT Sites
6. Agent Native Workflows and Resources Standardization
7. Skill Reuse as Key Company Metric
8. Company Reasoning Data as Strategic Gold
9. Shift from Public Leaderboards to Private Evals
10. Model Routing Becomes AI Industry Norm
11. Cheaper AI Intelligence, Anthropic Competition Heats
12. Fortune 100 AI Token Spend Efficiency
13. Compute Power as New AI Currency
14. Localized AI Controversies and Election Deepfakes
15. Mainstream AI Backlash and Content Detection
16. Math Benchmarks Solved by Advanced AI
17. Token Maxing Returns with Cost Decline
18. Open Agents Crash Risks and Cybersecurity
19. Recursive Self Improvement (RSI) in AI Development

Timestamps:

00:00 Starting the AI 101 series

03:35 Yearly AI predictions roundup

07:54 Using full duplex AI assistants

09:45 Talking vs. Typing to AI

13:45 Breaking down AI silos

19:03 Turning processes agent-native

22:32 Skill development and reuse in AI

24:09 Bringing Slack DMs into Channels

29:46 Dealing with AI usage limits

30:45 AI startups revolutionizing knowledge work

35:46 AI strategy in Fortune 500 companies

40:11 AI impact on local politics

41:58 Concerns Over AI Watermarking

46:52 Experiencing token budget challenges

51:05 Sergey Brin prioritizes RSI at Google

52:06 Discussing AI model improvements

55:24 Closing and subscription reminder

Keywords: 

AI predictions, AI trends, business AI strategy, proactive AI agents, reactive chat, AI operating systems, ChatGPT, Claude, Grokbot, voice and mobile AI control, full duplex agent, AI skills, skill reuse, manager threads, multiplayer AI, agent native, company reasoning data, private AI benchmarks, public leaderboards, private evals, model routing, AI token spend, open source models, compute scarcity, hardware scarcity, AI controversies, local AI data centers, AI deepfakes, AI backlash, AI content detectors, AI in politics, math solved by AI, token maxing, cyber defense, open agents, cybersecurity budget, recursive self improvement, RSI, Fortune 100 AI usage, AI workforce transformation, dashboard automation, AI for dashboards, no-code AI apps, business intelligence AI, automation skills, agent crashes, model overhang, vendor lock in, AI-powered cyberattacks, AI-driven skill creation, AI-enabled workflows, token efficiency, AI local hosting, cost-effective AI models, enterprise AI adoption, AI asset management, company AI metrics.

Transcript

SPEAKER_00 1:18

This could randomly be one of the most important shows I've ever recorded. Why? Well, I'm laying out the AI answers for your company for the entire year right here. Let me explain. Usually in December or January, I put on my AI predictions hat and tell you what's ahead. And for the past three years, I've done a pretty okay job in these prediction shows at telling you what's next. But the pace of AI is much different now. I mean, measurably, AI models are literally coming out about two to three times faster today than they were just nine months ago. So we can't wait until another four months in December or January to give you the roadmap ahead. So with the school year starting and with this upcoming start here series, we're about to restart from the beginning. I thought I'd just give you the answers today. Not the syllabus, not the take-home test, just the answers. So 19 quick points for you and your team to focus on, whatever the sector, whatever point you're at in your career, whatever type of work that you use AI for. I think today's show can be your answer sheet for the whole year. So you and your company are focusing on what's next, not what already happened. Let's get into it. If you're new here, welcome to Everyday AI. My name's Jordan Wilson, and well, we do this every day. It's your daily unedited, unscripted live stream, podcast, and free daily newsletter, helping business leaders like you and me not just keep up with what's happening in the AI world because it's too much for anyone human, but I help you get ahead to know what's coming so you can grow your company and your career. So it starts here, but make sure you go to our website at your everydayai.com, sign up for the free daily newsletter. We're we'll be recapping today's show and a whole lot more. So, as a reminder, we are taking a kind of break in our normal programming. So, since it is that back to school time and all the kids are going back to school, we're all gonna be doing some learning together on everyday AI. So, our start here series uh show that we kicked off earlier this year. We've been doing, you know, one or two shows a week, and we have 30 of them, and they're pretty amazing, and they're some of our most popular shows ever. So we're gonna be running them all back, the entire series starting Monday with the very first episode. So, yeah, a little break from our normal Monday to Friday schedule. But I guarantee you, if you just stick with the series from volume one, listen all the way to volume 30, whether you listen to one, some, or none of them, you're gonna know more about AI than like 99% of the other people at your company. So the series cuts through the jargon, cuts through the fluff. Whether you're a beginner, you around AI all the time, just it's simple. No computer science degree required, but class starts Monday, don't be late. All right, but let's now get into it and talk about those 19. Yeah, we have 19 of them. Uh, so these are the things that I think your company needs to be focusing on. Yeah. So for the school year here, right? So, yeah, classes and section set uh session. That's why we have the little uh, you know, yellow, the pencil yellow uh accent color here for my slides. So uh these are in no real order, right? I was just kind of jotting down some notes. Um, you know, sometimes I just uh am always keeping not like predictions, but a lot of my observations, because uh from doing this for three and a half years, literally spending my entire day every day uh reading about AI, exploring AI, talking to other very smart people about AI, right? I have a what I would say is a pretty accurate notebook of random ideas and thoughts. And I've been spending some time putting them all together. I came up with 19, right? So, you know, for my yearly prediction shows, I always do, you know, for 2024, it was 24, then 25, then 26. Well, we got 19. All right. So I don't know, maybe that's roughly how far we are through the year. But this is this is we're lining this up with the school year, right? We're starting in August. I think these should all still be fairly uh relevant, come, you know, when May, when school gets out. Uh, but let's start with number 19. So this is when reactive chat is dying and proactive agents are taking over. All right. And I also want you to think, right? Um, where are you at on the AI usage scale? Because maybe what I said is a shock. Maybe you're like, duh, it's been this way for a while, right? So as I rattle these off, right, keep in mind our audience is all over the place, right? We literally have AI leaders at the labs listening to this, and then we have people that have stumbled upon Chat GPT for the very first time and have never used it. All right. So keep that in mind as I walk through these. So maybe you've heard me mention some of these things before. Maybe you don't know what some of them are. So I'm gonna try to keep it simple. But I think over the next year, uh reactive chat is gonna start dying, right? It's the combination of not just uh agents, but just being able to uh very easily save and reuse skills. That is huge, right? Um, I think that Chat GPT work as an example is really indicative of this. It's it's no different necessarily than uh codecs, right? And codecs was kind of the, I would say, the uh one of the birthplaces um of the hardworking around the clock agent to that as well as uh Claude Code and Claude Cowork. Uh, but I think the new normal is just gonna be, you know, waking up and reviewing what your AI already shipped overnight. Um, and and and that piece is extremely important. All right, so if you're prompting less than what are you doing, all right, that's number 18, you're talking more. Uh, I think that number 18 kind of uh prediction, 18 here, I think voice and mobile are already becoming the default AI controls. Uh so if you haven't used uh ChatGPT uh's new feature yet, my gosh, uh Chat GPT voice is outstanding. What's crazy is this has been out for almost a month. And you know, normally the people that are quote unquote early talking about AI are starting to talk about it literally now. Um, but I think you know, um, to summarize this, I did a show on it a couple of weeks ago when it did uh first come out, but this is essentially kind of the combination of a voice and mobile becoming the default AI control. Uh yeah, you can go out and touch grass. And um, in in in my experience, I get a lot more done now when I take my iPhone out, right? And this is another, you know, open AI slash chat GPT. And I think you know, uh Claude has made some improvements to theirs, it's still not very good. Um, but I assume everyone is gonna have to follow suit because this is uh so so powerful um that you just have to have it, right? Even I think we saw a glimpse of it with, you know, Grok Grok bot that just came out. Um, but to be able to talk um to a uh full duplex agent, that means an agent that can listen and talk and uh at the same time, but one that also has access to your data and can look up information. It's literally faster than talking to a coworker, right? Um, because I can talk back and forth and it will respond to me. But then also while it's responding to me, it's gonna, you know, uh personalize everything through the data that it has connected, as well as go and look things up. So, you know, for me, this is like, you know, working with uh someone that's smarter than the smartest coworker I've ever worked with, right? But I think that this has to become eventually uh the main surface uh fairly soon, just because it is that good, right? And um, so you're like, okay, what does this mean? You know, just isn't that just mobile? Not necessarily, uh, because the desktop is obviously much more powerful than what you can accomplish on any mobile phone. But the way that this works, at least in Chat GPT voice, is well, you can open the ChatGPT app, click on remote, and then literally control your entire computer, right? So this is the Jarvis of AI where it can see what's on your computer, it can click things, it can open, save, file, shut, run commands, it can do anything. Uh, but I think that that is if if if you and your company have not already started exploring this, you should probably have your eyes on this because it is a much more fluid and natural way to work. I like to say it's kind of like a flow state. Um, right. I don't talk about this a lot, but sometimes, and maybe this is because I'm a former journalist, but sometimes when I'm a hands-on keyboard, maybe I'm trying to get things uh a little too right. You know, sometimes I don't just like to uh blurt out on the keyboard. Um, and that might interrupt kind of the natural flow state of what I actually want to communicate uh to an AI or to an agent. Obviously, when I'm talking, it's a little easier, I think, ultimately. And I think that holds true for a lot of people. There's actually some studies that show that you can much more effectively uh communicate your uh point when talking to AI agents while you're talking versus when you're texting because uh uh or typing. Because when you're typing, you're already putting, you know, multiple limiting filters on it uh because you know that you can only type so fast and your uh fingers cannot keep up with your brain. You know, your mouth can do a little bit better of a job. But I think number 18, that is definitely the next control. Number 17, uh, this is the manager of threads. So we've already seen this in um codecs and chat GPT work. Uh, we've seen a smaller version of this that was uh actually announced. Uh, I'm losing track of time. I think it was this week uh from Claude, right? But what does this mean? Um, I think we were trained for better or worse when working, whether inside, you know, Copilot, Gemini, ChatGPT, uh, Claude, et cetera, you know, that any conversation had its home, right? So, hey, I need to go back into that chat, right? And this was especially more important in the earlier days of prompt engineering. This is something I taught to thousands of students that took our free PPP course because this was the constraint, you know, probably up until a year ago, is the context really just lived in that single chat thread. It's not like that anymore, right? So now I think you have to think of manager threads replaces those one-off chats. So you could call those like a chief of staff, uh chief of staff um thread, right? So that's the way I think a lot of people set it up in a project. You can have, you know, an agent's MD file in any project. Um, and you can create an unlimited amount of threads or chats in there, but then any thread in uh you know, codecs and chat GPT can control any other thread. And again, uh that is an interface that all uh you know, Google, Claude, Grok, everyone else, you know, has either you know taken that as well, or they will have to soon, because that is um, you know, the the next step in terms of communication. And I think that's that's a smart way to do it, right? That's how it is with humans, right? If if you're sitting with you know your team, uh, you can talk to them at any point about anything that you all know. You don't have to wait for the marketing meeting. If you're sitting in the marketing team, is there? You can just talk about it. So this is a much more natural way to uh interface uh with AI agents. All right. Um, and you know, once those threads are managing threads, the next step is obvious. Well, you put people and agents in the same room. So that is number uh 16 on the list here. Multiplayer AI with humans and agents sharing the same workspace. And over the past two weeks, we've seen some great examples of that. I already mentioned Grokbot. Um, you know, so if you haven't seen this, it's only been out for like not even 72 hours yet, uh, right, but very similar to Buzz Um from Block, uh, you know, Jack Dorsey of uh Twitter Fame. Uh, but the same thing. So we've seen these two recent um interfaces come out that are multiplayer by default, so where you and your entire team uh can come in and chat with the same agent, uh, right. So we've seen glimpses of this, right? There's a Google leak uh about bringing some multiplayer capabilities. Um, obviously, you know, open AI uh inside ChatGPT, you know, they've had these group chats. So presumably a lot of these things are going to be moving over. Uh, but I do think that is the next interface. And why is that important for you and your business? Because I think siloed thinking when it comes to AI has been so deeply ingrained in our thought, and it is going to change quickly because what you can accomplish with a team in this environment is going to be so much better. Because, y'all, I've done training for large organizations, right? And there's always such a disconnect between kind of the AI champions and everyone else. And one of the things limiting uh, I think a lot of teams and keeping them from moving faster is things like sharing skills, right? It's it's it's these permissions things. So when you start to break down those walls, um, that's where I think the the gains are going to be compounding faster than they have so far. And we've seen it now, right? This is two strikes in the past two weeks. And I think the other big companies will be following suit. So number 16, multiplayer AI, you should be investing in it, right? So I'm not saying, you know, drop Claude and go to Grockbot. And I'm not saying, you know, drop Chad GPT and go try out Buzz. I'm not saying that, but you should be exploring in there. Um, so then when your AI operating system of choice um brings something like this, your team is ready to go. All right. So now that the whole team's in one room, yeah, look what they can build. Number 15, vibe operations. Y'all, uh, again, I keep leaning on uh you know some Chat GPT and OpenAI examples because I think, you know, probably in late 2025, early 2026, I think it was, you know, anthropic that was innovating. And I think ever since it's been open AI. But you know, let me just give one example. I think ChatGPT sites is probably the most underhyped and underused thing out there right now. Like I said, I think Chat GPT voice now, enough people are realizing like how good it is. Still, no one is talking about ChatGPT sites. And and and let me tell you what I mean by vibe operate, uh vibe operations. But this is when the whole company starts building. So if you don't know what ChatGPT sites are, um think of it like a a lovable or a a replet, right? So kind of uh a vibe platform um that you can, you know, create simple apps and dashboards and all of these things. But the difference is, well, you can change them and they live. So it's it's back end off, it's databases, all of these things. You don't have to know anything, right? I actually sent something to my wife, uh, when was it? I think it was today, and she's like, Oh wow, this is really cool. You know, it's Chat GPT sites because all this information, you know, it had photos and directories and listings and all these, you know, the ability to save these things. And it's like, oh wow, what is this? Oh, you built yeah, Chat GPT sites, right? But you know, that is available within Teams. So uh I I'm not saying that teams are going to, you know, replace their, you know, expensive software subscriptions. I'm not saying that, but I think that they're gonna start filling in the gaps. And, you know, instead, I think like dashboards are probably the simplest um explanation of that, right? So, you know, a lot of times you would have to, you know, get get your request in line with someone in you know, BI working in business intelligence who puts together the dashboards, and even then, you know, it's a lot of back and forth. Well, now you can literally just drop your files in there. Your team can collaborate. You don't have to know backend off servers, anything. It's just there. And you're you have live databases that you can collaborate on in a natural language. So I do think that we're gonna see a shift eventually toward vibe operations. It's one of those things I'm like literally still pulling my hair about. Like, why are people not using ChatGPT sites more? Uh, because there's no real true competitor, a single competitor, right? The closest would be, like I said, replit and lovable, but you don't have the same um power of users in data and connection, um, especially at the multiplayer level that you have with like Chat GPT teams or chat GPT enterprise that can get these sites up and running for their organization. Um, all right, next. Agent native. All right. Um this is I think more than a buzzword. All right. I've been thinking a lot about this and what it means to be agent native. And you know, I keep thinking back to you know, certain things in my background, working in marketing, you know, things like you know, mobile first or mobile responsive, all these, right? There's this this huge push, um, you know, to get since people were using the internet more on their phones, you know, 15 or so years ago, there's this big push. It took 10 years, right? But eventually the entire web became mobile responsive by default because the majority of people are accessing the internet on their phones and apps and everything, your business, everything had to be mobile responsive. The same thing is going to happen, but going agent native. All right. So I'm not just talking internally, I'm talking externally as well. So, what does that mean? So, resources that your team makes, they need to get repurposed in an agent native format. So, you know, all that prompting that you go through and do, you and your team, all right, all of it needs to turn into a skill because that is a skill that, or that is you um taking the outcome that you came to and making that outcome and the process that you got there agent native. In the same way that you know, teams would get together and build SOPs, um, right, that's uh what you should be doing here, but doing this for agents um according to the successful processes that you've uh brought into different AI operating systems. So that's one example. Another example is well, I think all websites should be marked out, right? Like there should be a button, right? Aside from having an agent first web, um, which I think is already happening in the background, right? I think many companies are gonna have two versions of their websites. The second version will probably be made automatically, or just converts everything to markdown so it's easier for agents to go through without having to be uh token inefficient AF and spend billions of tokens just to understand what's going on in the website. But I think in the same thing, simple user interfaces where you know you can say, you know, save this as markdown or convert this to markdown, you know, being able to easily turn your um you know, your company's websites or anything that you publish online um so an agent can more easily access it. So just two quick little examples there of being agent native. All right, that's number 14. So every once everything is kind of agent native, well, you need a new way to keep score. And I kind of already referenced this, but I think that skill use is going to boom and become a company metric. What do I mean by a company metric? Right, there's all these things, I think, through the course of AI, uh, that have been like weird metrics, right? So, like early, earlier on, right, uh, when AI was really hitting software development hard, it was all about the lock, right? It's all about the lines of code, right? Everyone's like, oh, I wrote uh 87 billion lines of code today. So, you know, I'm awesome. Um, and then you know, late 2025, early 2026, it was the token leaderboards, right? Those two things say absolutely nothing. I think one of the most important um measurables in AI is going to be skill reuse. All right. I I talked about this on the show uh last week, right? Uh, kind of how I AI, right? We did the entire thing. Um, I think I'd it's like 6,000. Uh I can I can actually bring it up here. Uh bring up my codex. Let's see. Okay, so I've used skills uh 6,700 times. Um, so it's a lot of skill use, um, right? Not could be more. Um, but I think that skill reuse is actually going to become one of the most important company metrics because when I said it is so easy to turn a repeat. Process into a skill. Let's say that you're working with a large language model, you're going back and forth, you're sharing data, you're doing all these things, and you finally get um an outcome or an output or deliverable that really works for you, your company, et cetera. Well, what do you do with it? Maybe you might turn it into a project. Okay, that's cool. But you need to turn that into a skill, not just because you can invoke it or an agent can invoke it on its own without you even asking it, right? Which is usually helpful. Sometimes it's not helpful. But the other thing is that is how you actually build AI IP for your company because skills are transferable, right? So if you're just stuffing everything, you know, into a project inside, you know, Google or Copilot or ChatGPT or Claude or whatever, that's it's not a it's not really an asset, right? Um, at that point, uh, that is a vendor lock-in. That's all you're doing at that point. So, you know, skill reuse is such an important uh part of of I think any measurement of a good someone that is being becoming um AI native is how much and how often you are reusing skills. Because in in theory, even if that skill is not always leading to a improved output or deliverable, it is undeniably saving an extraordinary amount of time, even versus normal AI use, let alone doing things the quote unquote analog way, right? Working on the internet, you analog. All right, so let's move on. So skills capture how works gets done. Number 12 is how you capture decisions getting made. And you know what? Finally, I think I had this in my prediction uh like two years ago, and I was too early, but we're finally starting to see this. Number 12 is company reasoning data finally surfacing as the goal that it is. So uh you're now seeing big companies like Zapier was one of the first that I saw talking about this, about the concept of bringing like Slack DMs out of the DMs and into the open and why and what does that mean and what does it signal? So, you know, essentially when you have an agent um in your Slack, uh most agents cannot see DMs, right? Um, aside from your own personal agent. Um, but when you start moving important conversations from individual DMs or groups of DMs into channels, right, not only does that help the decision making uh help others maybe see the decision-making process, but most importantly, it allows agents to understand how your company or how your individuals think and make decisions. Yes, that's obviously out in the open in normal channels, but the concept of uh, you know, and I actually talked with the Slack CMO about this on the very show about how important it is um to get kind of that human reasoning, that one-on-one human reasoning. A lot of times that is what the agent so sorely needs, but we're not giving it. We're just throwing more spreadsheets, you know, more skills and all these other things when really it just needs to be able to see um the nuance of a conversation between, you know, two, three, four decision makers that was maybe private. So that's just one small um example of company reasoning data finally being serviced as gold. But I think that we are gonna see that become bigger and bigger as agents are more easily uh to go out and crawl and understand different contexts, right? People are way like, wait, you know, even things like just yapping, right? Uh kind of going back to my my um my thought of you know, voice and mobile being the the surface that is gonna be the most important, you know, the amount that I can speak something uh versus type, there's so much more nuance and even reasoning uh that I can fit in there when I am just naturally speaking what's on my mind versus what just gets typed out. All right, let's keep going. Number 11, the public leaderboards. I'm not saying they're they're going to die completely, but they're gonna begin to die. And I think that private evals are gonna start to take over. I'll say this like 90% of benchmarks are just becoming useless. I mean, there's some that are obviously extremely important to look at regardless of the type of work that you and your company do, right? There's ones that are just great for general intelligence, right? Like uh GDP Val, uh the artificial analysis index, deep suite. I think some of those are just like no-brainer. So they're always going to be important. There's like hundreds of benchmarks, and people spend so much time looking and thinking uh about benchmarks. And aside from you know, benchmark uh bench maxing, uh, which is where you know frontier companies, you know, they overfit a model to perform very well on a specific benchmark, even if maybe humans or real work might not benefit uh as much. So I think that public uh leaderboards uh and some of these benchmarks are gonna decrease um very quickly. And I think the practical thing that's gonna happen is well, um company, uh companies, internal uh workbench, essentially, you know, kind of what I'm calling it, that's gonna become more important, right? Companies are gonna start, and some of the larger companies already have, right? They're actually, you know, creating these as you know, like public um evaluations, right? They're creating these as public benchmarks, things that they use for internally and then sharing them with others. I think those are gonna become more and more um important, but more and more common, right? I think it was like uh DoorDash or something that shared one of theirs, and I was like, okay, this is random. I'm like, no, this is actually very important because they took the models and it maybe didn't for them. Uh, you know, a certain model might have been much better or much worse than even in the corresponding evaluation, just because of the type of work that certain people do is so specific. All right, uh, and then next one kind of related. I think model routing is gonna become the norm, and eventually it is gonna become built in. Okay, so here's what I mean by that is most companies should stop defaulting to the top model because you don't need it. All right, you literally don't uh need it for most of your work, all right. And this also uh plays into kind of the um, you know, model or capability overhang. A year ago, I would have said the opposite. I would say absolutely use the best model available, even if it takes longer, because you need it for your work. You don't need it anymore, right? You don't need uh, you know, Fable 5 Max or GPT 5.6 soul max um to you know browse the web for you. You know, you don't need uh Opus 5 or you know uh Max or GPT 5.6 Terra to rewrite an email, right? You don't. You know, there's very capable models that cost a lot less. So if you're paying for it on the API side, you've already been through this, right? But even on the subscription side, right now, uh I think eventually the the free ride of AI, even in subscriptions, will become less subsidized, and you're gonna have to start dealing um you know with usage limits um and and quotas. So model routing, it it's gonna have to become a default, and it's actually very easy, right? I've I've made um skills that do this for me, right? I have a skill where you know I give a big project to uh you know GPT 56 soul and it breaks it up and it uses sub-agents at its discretion, but lower volume ones. So, you know, I really just have it work as uh working as the orchestrator in many instances. And, you know, I say if if it requires heavier lifting, you can do some of that middlework. But for a lot of those things, well, I'm just using, you know, maybe Seoul in the top 10 and last 10%, and then using a bunch of you know, smaller sub agents in between. But I think that's gonna become the norm. I think there's gonna be some uh and there already has been some interesting, you know, AI startups like merge and others that have come out that this is literally what they do, uh, right, because I think it's gonna save a lot of companies a lot of money. And that's something that companies are looking to do as the uh model overhang starts to become larger and larger. And what do I mean by that? I think that 90% of most knowledge work can be done with using only 10% of a models of that top top models intelligence and just wait, you know, until we get fable 5-1 and we get you know GPT six Astra or GPT-5-7 Astra, right? Whatever it is. Um our work as humans is not growing in capabilities at the same rate that the models are going, they're just not any. Yeah, like I said, two years ago, there were models that you know, the best models weren't capable to do all the work that we do. A year ago, I would say it was maybe in parity, right? But yeah, you needed that most powerful model to do your hardest work. And now, for most people's hardest work, like I said, you know, if you you know, sonnet might be good enough, right? GP or or you know, Opus. Um you know, opus might be overkill, you know, let alone Fable, right? You might just need Claude Sonnet, or you might just need, you know, GPT-56 uh Luna is plenty for many people, but that's why I think model routing is gonna become extremely important. All right, we're gonna pick up the pace so this doesn't turn into a two-hour show. Number nine, intelligence is gonna get cheaper, cheaper, cheaper, cheaper. And what that means, I think in the short term at least, is anthropic is gonna get squeezed, right? I've talked about this a lot on the show. If you if you care about, you know, who's who's your vendor, I'm not telling you to choose one or the other, but you have to understand what's going on, and it's been absolutely nutty the last two weeks. So, as an example, open AI used a sniffling of um, you know, recursive self-improvement. You know, they used GBD5. Right? They dropped the price of uh GBD5. So that model is nearly free. That is like nearly sonnet level. All right. Uh, but it's not just them. I mean, Grok46 that just came out, uh, so good, right? Uh, at least you know, first looks and cost per task. Um, you have Meta's Muse Spark 1.2, Kimmy K3, Quen 3.8. All of these models deliver 95 to 99 percent of the intelligence um of the anthropic models, but at five to 30 percent of the cost. Those aren't random numbers, those are the real numbers there. So, what do I mean by that? Well, intelligence is gonna get a lot cheaper, right? It used to be the thing where you know you could just choose your, you know, choose your horse and you would know that your horse would always, you know, stay competitive. I don't know why. It looks like anthropic um is not going to follow the trend here of um, you know, making intelligence cheaper and cheaper at the same time stronger and stronger. So um that's gonna be something to definitely keep an eye on. But I think as the school year drags on, I think intelligence is only going to get cheaper and cheaper. So your provider that you're sticking with, if they're not dropping their price, uh price per performance, yeah, might want to look another way. Just saying. All right. Uh, number eight. And this kind of all goes together, but I think Fortune 100 token spend is going to slow while usage explodes. And let me explain what I mean by that. Uh, similarly to, you know, I was talking about anthropic just selling tokens. Um, I think Fortune 100s, at least right now, are the only ones truly in the position uh to be able to, well, have the CapEx or uh the facilities to be able to run local models at scale. Because right now, um, you know, again, unless my joke is unless you have a data center sitting in your basement, you can't run these open uh models, right? The ones that, you know, oh, you know, everyone's like, oh my gosh, open source models are here. So, you know, all the big companies, what are they gonna sell anymore? Well, yeah, you can't run the open source models on consumer hardware. You need a couple million dollars worth of GPUs to run them. But that's where it's like, okay, well, the Fortune 100 companies, they've already started to do it. Um, they're gonna start to shift a lot of that lower hanging work, I think, to open models that they're running on-prem. Um, is that gonna trickle, trickle over to the rest of the Fortune 500 and uh, you know, your average everyday enterprise this year? Absolutely not. Um, but I mean, I think that you're gonna see token usage go up at these companies, but their token spend or the amount of tokens that they're buying from a provider go down. They're gonna stop uh, you know, renting AI and they're gonna start owning it. Uh, you know, and I think that that is gonna become more and more common over the years, but I think at least for this year, if you are listening at one of those Fortune 100s, you're probably already having those type of conversations. If you are, you know, maybe in the Fortune, you know, 101 to 500, right? These are just loose numbers. Um, it's something that you should probably start paying attention to and start crunching the numbers because I know a lot of companies that are spending um nine figures, you know, literally nine figures on AI. You know, so at a certain point, you have to say, okay, how much of that? Um, you know, let's let's look at our model routing. Are we routing models at all? How much are we overpaying, right? How many people are are using, you know, uh Fable Five to rewrite their emails, or how many people are using, you know, you know, Google Gemini, you know, 3.6 flash to you know classify something when you could be using a much more cost efficient model that is still um you know far capable to do the job. All right. Next, number seven, uh compute becomes currency. All right, uh you know, uh Nvidia C uh CEO Jensen Wong said something like this. Obviously, this is what his company does, uh, but I think it's the truth. Um, you know, there was a uh famous um, you know, uh Dario Modi uh interview, the CEO of Anthropic, I think it was from 2023, uh, where, you know, he was being asked about compute and investing in these data centers. And, you know, he essentially said, you know, we're we're gonna take it, we're gonna be cautious because we don't want to overspend. And what if the AI demand isn't there? And now obviously Anthropic is having to kind of pay for that underestimation uh of AI because they're having to overpay um, you know, and have all these other partnerships and they just aren't able to keep up with demand, which is why their servers uh and services are down more than anyone else in the industry. You know, on the flip side of that, open AI uh invested heavily early on and they're able to serve things at a much higher uptime up time uptime rate, right? But ultimately, um I think as more and more workflows, right, we're still early in the game, right? If you are listening to this, if you're using AI every single day, we are in that one percent. We are in the bubble. Most people are not there yet. So as the rest of the world catches up and says, Oh, wait, this AI thing is pretty essential, right? It's more, you know, it's as essential as, you know, the internet, it's as essential as electricity. As the rest of the business world, you know, stumbles across this over the next few years. I think this scarcity, right? Hardware scarcity becomes a real thing. Um, you know, but at the same time, I think at that point you say, oh, okay, we understand it now. It doesn't matter how good your model is if you can't run it, right? And I'm saying, you know, compute in the same way the companies like Anthropic, OpenAI, Microsoft, Google, Amazon, Meta, et cetera. Uh, but in the same way, compute to run things locally, right? That does become the new currency. Because if you, you know, are uh you know, a tech, you know, if you work at a big tech company and oh my gosh, well, we actually have servers, well, all of a sudden, you have the ability to um print money, right? Because everyone wants that compute. All right, let's keep going. Uh, number six, kind of the prediction here for the rest of the school year AI controversies are gonna hit your backyard. They're going very local. So between local data centers and AI deepfakes that I think are gonna hit really hard in the elections, I think AI controversy is going hyper local. We've already started to see it with the data centers. And let me just put this out there there's a lot of bad information out there about data centers. I'm not gonna rally on that. I think there's plenty of people having those conversations. I'm just trying to more focus on the practical use of AI, but I think it is going to become a dinner time conversation uh at some point this year between, you know, the AI data centers and, you know, certain communities, you know, saying they don't want them, certain communities welcoming them in. It's gonna create, I think, um, you know, income disparities in places that maybe didn't see it before, swinging both ways. Um, but I think ultimately where it's gonna hit hardest is local and state politics, y'all. This is my previous life. I was uh uh you know a politics reporter uh for the Chicago Sun-Times. Um at the big level, right? You know, presidential race, everyone knows when you know AI is being used, even AI that looks real um or sounds real locally, I think, right? So statewide races, I think you we're gonna see dozens of stories about people using AI. And a lot of people aren't gonna figure out until it's too late, but for bad reasons, bad actor AI, right? People, you know, creating uh fake audio of their competitors, you know, videos um, you know, that they're gonna use in ads that didn't actually happen, it is gonna become commonplace. And I don't think most people will be able to tell the difference, which is why I think the AI controversies are gonna be hyper local. All right. Similarly, well, I think the AI backlash is going to become mainstream. Uh, so as the uh shifts start stacking up, um, I think it's gonna start with the work slop avalanche. And I think it's already kind of started. Um, and a couple of recent developments are gonna think, I think, um exasperate this. So, you know, as an example, uh Anthropic uh kind of announced to uh adhere to some EU regulations that they're gonna start uh putting in some invisible watermarking in text. So even if you copy and paste it and try to do all this stuff, you know, um, but essentially, I think that uh uh kind of movement has also led to the resurgence of the you know AI content detectors, which I'm not a fan of because most of them are uh not good. Uh, but I think we are gonna see this this weird paradigm because every single company is rightfully so, um, you know, trying to implement AI from top to bottom. Yet, you know, now I think more than ever, you're gonna see people getting called out for using AI, right? Whether it's you know, like this clawed watermarking or some other um, you know, you know, AI text detection. Um, but there's gonna be, I think, a huge backlash against using AI. I even found myself, right? I was I was looking for uh what was it, uh a shirt or something online. Um, and you know, I noticed that they were using all you know AI models. And it's like I knew that 95% of people weren't gonna be able to tell. It wasn't the best AI uh images, but you know, I saw that and I'm just like, nope, next, right? So even me, right? And people always think that I'm oh AI, everything, and I'm not, um, right. I it's just like I think it should always be tastefully done. But I think the AI backlash, because it is more and more accessible and more and more people are going to use it, um, it's gonna hit hard. But I think it's gonna go mainstream. Like I talked about this years ago, but uh, I think we're gonna start to see it uh, where you know people are gonna start putting things out as human-made, and that's gonna become like a trendy thing. But I think the AI backlash is gonna hit hard. Uh, number four, and I have no clue what this means, but one of my predictions is gonna be that math is solved, right? I still don't quite understand how there's all these dozens of math theories that, you know, people, you know, the smartest mathematicians in the world have spent their careers on and it's they they've been unsolved. For forever, right? People have been working on these problems for hundreds of years. I think math gets solved, right? I don't know what it means, right? As an example, uh, the unreleased GPT, uh, whatever number it was, Astra, which is their next family of models on top of Seoul. Uh, it knocked out like 10 problems with published proofs. Um, but I think math in general is gonna get solved. And that's gonna lead to a lot of things that are above my pay grade of understanding. Um, obviously, uh great things, uh medicine development, protein synthesis, all these other things that can lead to positives. But I also do assume that when math gets solved, um, and that's gonna be able to, I think, um, on your open models that can run on consumer devices are gonna have math solved uh probably within the year. So I'm guessing there's gonna be some downsides to that as well, right? I don't know, like cracking crypto, right? We're still probably uh a way off on that. Um, but I think that there's gonna be some downsides to math being solved. Um, but in general, I think math benchmarks are no longer gonna be a meaningful benchmark. And if anything, this should just tell you how good uh AI has gotten. Because three years ago, right, when ChatGPT kind of uh you know started to become consumer mainstream, it couldn't consistently solve two plus two, right? Now I think it's literally gonna solve math. All right. Next, three token maxing coming back. All right. So this kind of ties in with uh one of mine earlier about intelligence is coming too cheap. But essentially, right, token maxing, where it's like, let's spend as many tokens as possible, was very much in vogue uh in, you know, like December 2025 through like February. And then there was the whiplash of uh, oh wait, no, we can't anymore because whoops, we're spending way more money than we thought. We were trying to encourage employees to use AI, and they were all spending billions of tokens, you know, spinning up useless things. So now there's been this shift um toward uh kind of value maxing or token efficiency. But now I think we're going back, right? Just like the mom jeans, you you know, they were hot, they were gone, they're back. I think token maxing is already going to come back. And I mean, look at the last week. I mean, how could you not? Look at Luna. I am spending more tokens than ever, um, right, because the tokens are getting cheaper. All right. So that's why I think token maxing is actually going to return, right? Experimenting, running more and more agents. So I think we went through this weird phase of like, you know, three to five months where it was like token budgets are getting tight, right? Spend is getting tight, which was reactionary. It was not looking forward into the future because in if you were looking forward in the future, you see intelligence is going to come down, right? So this this this concept of token maxing, um, or you know, or sorry, the concept of token efficiency or value maxing still hasn't fully caught on, right? So it's already made a complete cycle. Um, I think now. So get ahead of it. Because I think still this this the the token reckoning hasn't fully hit outside of like Silicon Valley, right? It has in some places. So if you're listening to this, get ahead. And you know, if someone's, you know, some smart guy comes in, Bill comes in on Tuesday, and he's like, All right, well, I don't know if you guys know this, but tokens are getting out of control. So we have to, you know, talk about efficiency. You can say, All right, Bill, uh, you know, it's no longer February 2026. The tokens are actually cheaper than ever. And the tokens now compared to what they could do, uh, you know, four months ago, night and day. Token maxing coming back, book it. Number two, the open agents are gonna crash in 2027. All right. This is the full school year. All right, we're we're going all the way into uh May, May or June 2027. Open agents are gonna crash everything. I talked about agent crashes um on my uh 2026 AI prediction and roadmap series, which obviously they've been crashing, right? We had all the stories, the the uh open AI, hugging face, uh Kimmy K3 anthropics models, every so the agents are crashing right now, but the open crashes are what's going to really matter. So I had a show on this the other day, if you want to go back and listen to it. But I think essentially the open models today aren't good enough to cause chaos yet in bad actors' hands. But the open models that we shouldn't be getting around quarter four, they will be because those models are gonna be fable level. Those models are gonna be, you know, Astra, potentially Astra level, at least GPT-5-6 soul level. So what does that mean? Uh, that means that they're gonna be smart enough or conniving enough when bad actors are using them to be able to skirt around the guardrails that are built in. And obviously, with open models, once you release them, you can't rein them in, right? You can't take uh, you know, Kimi K3.1 or you know, Quen 3.9. You can't take them offline, right? Once they're out, they're out. Um, and obviously, because they're open models, uh, you can fork them, you can fine-tune them, you can do what you can to bring the guardrails down. And I think that is gonna happen at scale. Um, just FYI. So, what does that mean? I think cyber defense and cybersecurity, um, especially of the AI variety, it's gonna be the fastest growing sector um in 2027, and it's not even gonna be close. It is not even gonna be close. Assuming, right, that it's gonna happen at the rate that it's gonna happen. I mean, even small businesses, you know, SMBs, smaller enterprises that normally wouldn't have a strong cybersecurity presence. I'm not saying that they're gonna go hire a cybersecurity team of 10, but all of a sudden it's gonna be a budget item. If you're not gonna hire people, you're gonna have to hire a third-party company and it is not gonna be cheap. So start penciling that in now. The everyday average business, right? Your little, you know, not saying little, but you know, your average company with 200 employees, 500 employees, right, that have maybe never been the victim of a cyber attack. When agents, when open agents are gonna be able to find vulnerabilities in your CRM vulnerabilities in your payment systems, they will be able to wreak havoc. You have to be prepared for it now. And then number one, last but not least, and this kind of ties a lot of the stuff together, RSI is on the horizon. Recursive self-improvement. I also had a recent show about this, but actually some more recent developments. So, Sergey Brin, the most one of the most famous people in AI ever, who now seems to have a little bit more. Uh, you know, he's always been at the top of the Google AI food chain. But now it seems like um, you know, with some recent leadership shakeups at Google, um, you know, he might have a little bit more, I wouldn't say sway or pull, um, but it seems like he's gonna be uh a much more consequential figure um in Google with some of their other big names gone. But, you know, just recently he just declared that recursive self-improvement is a top priority for Google Gemini and kind of um directing and wanting companies to go there. And we've already seen this. Um, you know, we've already seen glimpses of this. I've already mentioned, you know, GVT56 Soul as an example, making uh its smaller models more optimized and cheaper. Um and we've already seen stories that you know, companies say that their models that are coming out now were um, you know, um whether going through post-training or, you know, they're not always um giving the details, but today's models are starting to help build tomorrow's models. Um, and with recursive self-improvement, that makes everything else. The other 18 on this list go that much faster. All right, that's a wrap. I'm gonna go through these one more time, rapid style. So you don't forget. Number 19, reactive chat is gonna start dying and proactive agents are gonna take over. Uh, number 18, voice mode and mobile become the default AI interface. Number 17, manager threads are gonna replace one-off chats. Number 16, multiplayer AI, humans and agents are gonna share one workspace. Number 15, vibe operations, the whole company is gonna start building. Number 14, everything you make goes agent native. Number 13, skill reuse becomes a company metric. Number 12, company reasoning data finally surfaces as gold. Number 11, public leaderboards die and private evals are gonna take over. Number 10, model routing becomes the norm and built in. Number nine, intelligence gets cheap and anthropic gets squeezed. Number eight, fortune 100's token spend slows while usage explodes. Um, number seven, compute becomes currency. Number six, AI controversies hit your backyard. Number five, the AI backlash goes mainstream. Number four, math is solved, whatever that means by AI. Number three, the return of token maxing. Number two, open agents crash in 2027. And number one, RSI is on the horizon. All right, that is a wrap for today's show. But remember, a quick announcement. Now you're ready. I gave you the answers. And maybe some of this was a little over your head. And you're like, wait, I'm kind of new here. I need some of the basics. Well, go back to the basics because, like I said earlier, uh, we're we're taking a little break from our normal Monday to Friday schedule uh to replay the entire and restart the start here series. So this is a series made for both beginners and people who are using AI every single day and leaders in their organization. We're gonna play episodes one through 30 in order starting Monday. So if this got your brain turning and you're like, okay, these are some good things for our company to talk about. Or if you were like, wait, I need to know more about you know the these open models. I need to learn more about you know some of these topics on agents that you talked about. Well, in the start here series, we tackled most of these things all in depth, uh, at a higher level, more detail, more examples, more use cases for your company to grow. So that's it. Make sure to go back to school with us starting Monday. So that's a wrap. I hope this was helpful. If so, please, if you haven't already, subscribe to the podcast, then go to your everydayai.com. Thanks for tuning in. See you back tomorrow and every day for more everyday AI. Thanks, y'all.