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2026 AI Roadmap: The Exact Shortcuts and Disruptions Business Leaders Must Prepare For
AI discussions often hover in broad, future-focused speculation, but at this moment, there are immediate and targeted insights that can drive competitive advantage—or expose vulnerabilities—in businesses. This detailed synthesis translates current AI trajectories into explicit business action, drawing directly from hundreds of high-level conversations and granular observations by AI practitioners, as summarized in recent, highly distilled predictions for 2026.
AI Software Trends: Disposable Development and the Decline of Long-Term Licensing Models
Enterprise IT traditionally measured software lifecycle in years, if not decades. The coming trend is the normalization of disposable software—highly targeted applications, rapidly built for one-time campaigns, data migrations, or finite projects, and then discarded as soon as their purpose is served. This fundamentally alters the economics of software:
Lifecycle Compression: What once took years now takes weeks or days. Code can be generated for specific tasks, updated by AI, and retired without legacy concerns.
License Reassessment: With companies often paying for five to ten times the features they need, there is a shift toward building only what is essential, saving operational expenditure and reducing software “debt.”
Governing this “sprawl” demands attention, but for agile businesses willing to adapt, it is an opportunity to cut costs and increase operational velocity.
Multi-Agent AI Societies: Changing the Structure of Enterprise Operations
For years, AI has been conceptualized as a singular assistant. By 2026, multi-agent societies will become the norm in enterprise deployments:
Each agent is specialized: one plans, one executes, another verifies, and still others handle design or data analysis.
This mirrors high-performing human teams but with workload distributed entirely among digital agents that negotiate, collaborate, and self-manage.
Businesses adopting these architectures will see AI handling entire sales reports, proposal designs, and detailed analytics generation—autonomously dividing work among agents and only surfacing the result for human review or oversight.
AI Native Advertising: A Premium on User Intent
Advertising platforms built on AI assistants—especially those integrated into workplace or personal productivity flows—command markedly higher CPMs (cost per thousand impressions) via explicit user intent. Unlike search engines powered by broad keyword queries, AI assistants capture detailed psychological intent as users disclose their goals, needs, and constraints directly to the assistant.
This shift will:
Enable companies to target users at the exact moment of high intent, decreasing waste in ad spend.
Transform the traditional funnel; there is less need for top-of-funnel prospecting when high-converting leads are instantly identifiable within AI conversations.
Companies must recalibrate marketing strategies to account for these ultra-premium channels and begin experimenting early to compete before these environments become saturated.
The Consulting and Professional Services Shake-Up
High-cost professional service business models face imminent disruption:
AI Agents Replace Junior Ranks: Agents now outperform entry-level consulting and legal work, handling research, synthesis, and even deliverable creation—compressing weeks of billable work into hours.
AI Flanker Brands: Major firms will launch separate, AI-driven low-cost services to retain market share under pressure from specialized, AI-native competitors.
Client Transparency Expectations: Clients are getting wise—demanding to know how much work is AI-augmented and expecting cost reductions to reflect these efficiencies.
Early signals indicate that only firms that restructure workflows and deploy transparent, value-driven pricing alongside their “premium” advisory arms will avoid erosion of both margins and reputation.
Data Integrity: The Looming Slop Debt Crisis
One of the most acute risks on the horizon is the slop debt crisis—where polluted AI training data, much of it generated by other models, pollutes the foundation of LLM (Large Language Model) outputs. This contamination includes inaccurate information dressed as authoritative, leading to silent but severe cascading validity issues in AI-generated insights.
Enterprises relying on AI for critical functions—especially those requiring reliable, up-to-date knowledge—face the need for vigilant data hygiene and possibly dedicated curation of private datasets.
There is an emergent business case for proprietary data lakes and robust human validation, ensuring AI models remain trustworthy as public data continues to degrade.
Coding’s New Reality: The Hand-off from Humans to AI
In leading AI organizations, executives are signaling a tipping point: AI now writes the majority of internal production code. This is not speculative; it is a documented operational fact in several labs.
Businesses with technical teams must plan to upskill and restructure, shifting from manual code creation to oversight of AI-generated code and “context engineering”—the art of feeding and maintaining the right business logic, rules, and security into the system.
There is a window of opportunity now for organizations to experiment, develop governance, and secure institutional knowledge before this shift leaves them uncompetitive.
Context Engines: From Prompt Libraries to Portable, Versioned Knowledge
Prompt libraries—collections of reusable instructions for language models—have limited lifespan and are brittle to changes in AI architectures. The future lies in portable context engines:
Collections of versioned, modular, and auditable knowledge assets (often as markdown files) that travel across platforms and models.
These offer resilience, auditability, and continuous improvement, forming the backbone of cross-tool AI operations.
Organizations not yet formalizing their own context management risk being left behind as models and platforms become more interchangeable and context-dependent.
Final Word: Unlearning and Rebuilding as the 2026 Imperative
The core message for competitive businesses is not to incrementally adapt, but to unlearn entrenched processes and rebuild with new AI-native structures. 2026 will not reward those who simply “upskill” existing teams or layer AI over current workflows. Instead, the winners will be those who leverage disposable software, multi-agent workflows, premium intent channels, and proprietary data hygiene—supported by leaders who recognize the end of “business as usual.”
Key Takeaway: The most valuable AI shortcut is not just awareness, but specific, focused action in the key areas outlined above—starting now, not later.
Topics Covered in This Episode:
- OpenAI Consumer Hardware Predictions 2026
- Disposable Software Adoption in Enterprises
- NotebookLM as Fifth Core AI Platform
- AI Native Ads Maintain Premium Pricing
- Multi-Agent Societies: Enterprise Default Architecture
- Microsoft Copilot Usability Reset Forecast
- Big Four Consulting AI-Driven Restructuring
- Vibe Coding Rebranded as Agentic Orchestration
- Professional Services Launch AI Flanker Brands
- Slop Debt Crisis and LLM Data Integrity
- Frontier Labs: Humans Rarely Write Code
- Portable Context Engines Replace Prompt Libraries
- GDP-Val Benchmark Scores Surpass 80%
- 2026 AI Roadmap: Unlearning and Rebuilding
Episode Transcript
Midroll [00:00:00]:
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.
Jordan Wilson [00:00:16]:
You don't have thousands of hours a year to devote to staying on top of AI developments, but you need that much time. That's because you've gotta follow everything, but 95% of the AI movement is sheer distraction, but that 5% is a force multiplier for your work. But to know the difference, you've gotta be an AI news hawk, and you can't do anything else except stayed glue to the AI developments. Or you could listen to 700 plus everyday AI podcasts and go read every single newsletter or crazy thought. You just listen to our twenty twenty six AI predictions and road map series. That's because between today's show and yesterday's, we're not only condensing down thousands of hours of AI insights, but we're projecting the trends on what's coming next based on all of those hours. And we're also building you the road map as part of that AI predictions and road map series. So today, we're kicking off part two of the biggest AI shortcut there is.
Jordan Wilson [00:01:25]:
So make sure you check out part one from yesterday. Alright. Enough. Let's get into it. Welcome to everyday AI. My name is Jordan Wilson. If you are brand new here, well, we've been doing this thing every single day for three plus years now. And it's your daily guide to stay on top of what matters in AI with our unedited, unscripted daily livestream podcast and free daily newsletter.
Jordan Wilson [00:01:49]:
So it starts here with the podcast. But if you want the actionable next steps, make sure you go sign up for the free daily newsletter at youreverydayai.com. We're gonna be recapping today's show and giving you everything else you need to know to stay in the loop. Speaking of everything you need to know, well, if you listen to our show all the time, you know, I'm lucky enough to get to talk to hundreds of the smartest people in AI. And what I've realized after a lot of conversations, right, 700 plus podcast episodes is, well, I'm able to, kind of grab these nuggets of wisdom from a lot of very smart people that are building AI. And I obviously have, hundreds of conversations every single year that aren't on the show, and I've realized I'm able to kind of connect these dots, right, by just stealing these insights from the smartest people in the world. And then I have time to reflect, and I'm like, oh, wait. So throughout the year, I always, have a working notes file for this very show.
Jordan Wilson [00:02:44]:
So I literally plan for this thing every year. I'm already planning for next year's. So, just keep that in mind. That's where this come from comes from, and I always audit myself too. Because sometimes I have these crazy predictions, and sometimes they mostly end up coming true. So if you wanna go check on my audit work from last year, make sure you go check out episodes six seventy four and six seventy six. That's when I did a, kind of an audit or a rewind on my twenty twenty five, AI predictions. And it's actually, aside from wow.
Jordan Wilson [00:03:16]:
I was very much on target even though they were kinda crazy predictions. Aside from that, it's a great catch up to get, you know, an entire year of AI developments, in a couple of minutes. Alright. And then like I said, make sure you go check out yesterday's episode volume one. There, we covered a lot, so make sure you go check that out. And also, you're probably gonna wanna, repost this on LinkedIn. Just saying. So if you are listening on the podcast, make sure to check your show notes.
Jordan Wilson [00:03:44]:
We always, have a link to the LinkedIn live stream. So, go repost this, because I started with, I I forgot the number. It was, like, 150 or 200, different AI predictions, and I boiled it boiled it down to, like, my top 53 or something like that. So I it's twenty twenty six. So I'm sharing 26 of them, but if you want them all, just go repost this show on LinkedIn. I have an incredible guide, with even more insights on the one that I'm on the ones that I'm sharing. I have more insights on the twenty sixth that I'm sharing that I don't even have time to get to. So that is in our bonus guide, the, twenty twenty six AI predictions and roadmap series bonus guide.
Jordan Wilson [00:04:25]:
Go repost that, and I'll send it to you. Alright. Let's get straight to the predictions. These are no particular order, FYI. So let's, like I said yesterday, we did one through 13. Today, we're doing 14 through 26. So number 14, OpenAI ships no consumer hardware this year. Number 15, disposable software becomes routine practice.
Jordan Wilson [00:04:46]:
16, NotebookLM becomes the fifth core AI platform. 17, AI native ads maintain premium intent pricing. 18, multi agent societies become enterprise default architecture. 19, Microsoft launches a Copilot WorksNow reset campaign. 20, big four consulting announces an AI driven restructuring. 21, VibeCoding rebrands as agentic software orchestration or something similar. 22, professional services launch AI Flanker brands. And at 23, the slop debt crisis makes some large language models unusable.
Jordan Wilson [00:05:26]:
Spicy. Oh, there's more. It's not 2023. I'm tired. '24 frontier lab, a frontier lab publicly, declares humans rarely write code. 25 portable context engines replace prompt libraries. And at '26, we're gonna see GDP val, scores that cross the 80% threshold. All right.
Jordan Wilson [00:05:51]:
Fun stuff. Let's get into it. Number 14, open AI is shipping nothing when it comes to hardware. All right. So there's been a lot of buzz and a lot of rumors on this third device. Right? That OpenAI is, reportedly working on, and we've seen reports over the past year. Seemingly, they've nabbed more than a dozen Apple hardware leaders. You know, they have Johnny Ives, you know, IO company, a design company, but I don't think we're gonna see any of it.
Jordan Wilson [00:06:27]:
And I think one of the reasons well, it's the code red that happened in the 2025. Right? Reportedly, OpenAI put out this code red, right, that they said, wait. Google is, starting to eat everyone's lunch. We gotta get our stuff together. And granted, on the software side, on the AI side, OpenAI has been shipping a lot. And I actually think one of my predictions I didn't get to, is on Atlas. I think Atlas is actually gonna be a standout product for OpenAI. It's one of the few products that they are updating routinely, both Atlas and codecs.
Jordan Wilson [00:07:03]:
Right? Like, port agent mode doesn't get any love. GPTs are ignored. You know, not actually ignored, but I'm saying, you know, they have a lot going on in the software side, but it was all of these, I think, hardware, you know, endeavors with this third device. Right? It's it's a laptop. It's a phone, and then whatever OpenAI, right, says that we need. Right? Whether it's a hockey puck we stick in our pockets or a pen or whatever. But I don't think we're gonna see any hardware. I think that their competitive leverage is going to shift back to just competing on model capabilities and distribution.
Jordan Wilson [00:07:40]:
And on the distribution side, that's where they've continued to, keep I I won't say an untouchable lead, but a sizable lead, over anthropic Google and Microsoft. And on the model capability side, not quite the same story there. Right? Because, if you went back eighteen months, no one was touching OpenAI. Now, you know, it's it's it's a race. Right? I think at any point, like I said on yesterday's show, Google is going to what's actually funny, that prediction didn't take long, FYI. It was literally about five hours after that show when I said at at any point, Google could, you know, because at the time I say at the time like it it was years ago. But at the time of the show, they did not have the top model on most text benchmarks. And then five literally five hours after the show, they released, a new version, Gemini three pro deep think.
Jordan Wilson [00:08:37]:
Right? And it swept, you know, all of the important stats. So when I say Google at lead time, they they want to. They can come in, on the benchmark side, on the model capability side and be the leader. They will. But I I do still think it's always gonna be one a and one b, between, at least when it comes to general general purpose between Google and OpenAI. But I don't think I think that, some of these other endeavors, right, all these, financing deals that, you know, kind of, distracted all of the, you know, kind of the NBA transactions of, you know, different people going different places. I think it all became maybe too much for OpenAI, and it seems like they've recentered themselves. So I know a lot of people are looking forward to something from the hardware side, but hardware has brutally low margins.
Jordan Wilson [00:09:24]:
Right? And a lot of the, you know, hardware AI plays that came out in 2024 and 2025 were a disaster. And maybe that's a signal that, the consumer industry isn't quite ready for that at scale. Right? And, you know, maybe they wanna be the electricity company and not the toaster company. Right. Next one, fifteen. Disposable software becomes a routine practice. So here's the prediction in 2026 enterprises are going to regularly build and then discard short lived applications created for specific one time tasks. Right? I think teams are just gonna create full, fully working, software tools for certain campaigns for certain, migrations.
Jordan Wilson [00:10:14]:
But I think the life cycle for some software is going to go from years to weeks. Right. I think that government governance is gonna have to adapt to that kind of sprawl as well. But I think these disposable patterns are gonna become standardized. Right? I think let me tell you this. I don't think people are gonna believe me, but I've always been a software nut. I've used and I'm not exaggerating when I say this. I've used thousands with an s thousands of pieces of software.
Jordan Wilson [00:10:46]:
At all times now, for the most part, when I'm doing this show, I have, software being written for me. Right? Like, right now, I literally, let me see if I if I have both codex and cloud code. Yeah. I have codex and cloud code right now writing software for me. At at this point for me right now, I don't think it's disposable for me. I think it it will end up being semi disposable. But think of how a lot of software has worked traditionally. Sometimes you spend so much time scoping it.
Jordan Wilson [00:11:21]:
You tie you you know, you sign up for all these trials, and then you find, like, You know, you end up making concessions, and you're like, okay. Well, we actually just needed these three features, but you gotta pay, you know, $500 a month, and you get all these other things that we don't necessarily want. So then you spend time saying, like, okay. Well, what's gonna be worth it? We have to buy it even though we only need three out of the 10 features that everyone's offering. So we gotta buy the other seven. So which of the other seven can we put into place? We gotta test it out. Right? Versus now, I mean, my gosh. I've been talking about this now for two minutes.
Jordan Wilson [00:11:50]:
You could have already built a simple version of at least one of those three features that you need. So I do think that we are gonna be coming into this disposable, software, series. And whatever anyone says, right, I've I've I've seen some, some things on the Internet pick up steam lately. Right? Everyone's like, oh, you know, vibe coding's great, but, you know, you end up, you know, going back and forth, you you know, with with whatever models for, you know, hours or days. I mean, if you're trying to build, you you know, a billion dollar company, like, of course. If you need something that solves an annoying task, that you do over and over, or if you're just trying to, you know, to use some terminology that I go to sometimes. Right? Duct tape. I say sometimes humans are the duct tape between AI.
Jordan Wilson [00:12:42]:
Right? But you can build, duct tape disposable apps with AI fairly quickly. It's funny. I typed into codex earlier today, an idea for an app, and then I went upstairs and got a drink. I came down. It was done. Right? Was it perfect? No. Was it working? Yes. It was something I was paying, you know, $10 a month for.
Jordan Wilson [00:13:09]:
So, you you know, I do think people when when when we talk about vibe coded software, the defacto response to everyone in the enterprise is, you know, everyone's like, oh, well, what? You think your company is gonna, you know, vibe code Salesforce? It's like, no. Absolutely not. Right? But think of maybe how many, you know, Salesforce adjacent products people might have. Right? Or, you you know, plugins that people pay for. Right? I I remember paying a lot of money for different plugins that work alongside different types of software. I think it's things like that or, you know, pieces of, software that you used to use that are no longer supported, like I said. Or, hey. We have these five again, five five pieces of software.
Jordan Wilson [00:13:54]:
This is, I think, is gonna have the real enterprise, utility in the long run. When you have expensive software that you're actually using, multiple pieces, and you're gonna create, you know, a version of just what you need. Right? If you're only using 20%, but you're overpaying the 80% for five pieces at that point, it is worth, you know, putting a couple months of Vibe coded development if it ends up safe. Right? Especially if you're paying per seat. Right? A lot of people, their software debt, so not their software utility, what they're actually getting out of it, but their software debt is in the millions of dollars. So, yes, disposable software is going to become a thing. I think enterprise vibe coded software, will still happen. It's already happening, but I don't think that's going to be a norm.
Jordan Wilson [00:14:41]:
But I think if you're listening to this show, come talk to me next year. You're gonna have at least a pea couple pieces. Right? If you're already mildly technical and you listen to the show every day, you're gonna have plenty of disposable software. You're like, oh, yeah. I use this for a couple days. Right? I have some sometimes that I just use once and then I'm done. Because, you you know, it's like, oh, I put in a certain, you know, model or, you know, it's a process. I'm I'm I that I know I have to do once for a big project.
Jordan Wilson [00:15:09]:
It's just like, okay. I'll, you know, use, like, an opal or, you know, something easy like that. Right? And it's like, okay. It's done already. Right? And I think especially now with, some of the new Gemini models and, you know, the Gemini CLI, with quad code on the desktop with with codex, it's just too easy not to. Alright. 16. NotebookLM becomes the fifth core AI platform.
Jordan Wilson [00:15:32]:
This might be one of the more random and kind of out there, but I think okay. So, obviously, when I say big core platforms, I'm not talking about back end APIs. I'm talking about front end large language models. So for the most part, right, that's Microsoft Copilot, Chat GPT, Anthropic Claude, and Google Gemini. So, yeah, I'm not an idiot. I understand that Nobel is under Google, and it is powered by Gemini, but it is a completely different product. And I think right? There's been times. Right? If if if if you've been a Google, user for a long time, like, you know, Google comes out with a lot.
Jordan Wilson [00:16:16]:
There's a lot out there on the Google AI side that you've probably never even heard of. Go look at like, go check out Google's, AI labs. It's insane. So good. Right? But a lot of stuff just well, you know, they'll release it, and then that's done. Right? That's not how notebook LM is. Their team is cooking. Right? I'm talking to a couple members of their teams, you know, specifically of the notebook LM team, just, you know, on DMs in different places and just, you you know, getting some more, insight and intel in terms of what they're working on and just how hard they're cooking on these products only.
Jordan Wilson [00:16:51]:
This isn't a a a side project for a couple of people. This, I think, is has been one of Google's main drivers, when it comes to gaining some of that market share, from OpenAI. It's been notebook l m. And I do think that from an outsider's perspective, I think notebook l m is gonna come into the conversation where it becomes a verb. Right? Oh, you better notebook that. I think that's where we're gonna be. And people there's gonna be people, I think, in companies specifically with some things that are coming out. Okay? There's gonna be companies that are using NotebookLM that they're not even gonna know it's Google.
Jordan Wilson [00:17:26]:
And then they're gonna see this, oh, direct integration. You know, this, you know, notebook and, Google Gemini come on kinda backward, compatibility, and they're gonna be like, oh, that's real cool. Right? Right? I think it's gonna become that big and that well known, and that useful and still, which is crazy to say that's separate from Google Gemini. Right? Some of the best features I think of any AI, any consumer AI we've ever seen have been have come from NotebookLM. The the AI audio overviews and the deep dives, the, NotebookLM, with their, nano banana, slides, the video overviews, right? The fact that you can dump in hundreds of sources, thousands of pages, and it will create a custom video with a voice with amazing graphics and illustrations. And it's free and it takes a couple of minutes. Right? I think especially with people's attention spans. Right? Like, like, you gotta call it what it is.
Jordan Wilson [00:18:31]:
I was actually, one of my podcast episodes this year, talking to a chief evangelist, Richard, from Google. You know, he had this little line that stuck with me. He said demos over memos. Right? I think memos in general is they're gonna die. Right? And I think, unfortunately, as a former journalist, this hurts me to say the power of just the written word, I think, is losing traction. And what do most large language models excel at? Right? Aside from code. Right? It's the written word. Ultimately, I think what people care about, what is going to engage them is what we're seeing at a notebook LM, which I think is gonna be one of the key factors that leads to its rise.
Jordan Wilson [00:19:11]:
That is the multimodality, the personalization, customization at scale, right, to consume information in a way that feels very personal and relatable, but also extremely high quality. Alright. Next, 17, AI native ads are going to maintain their premium intent pricing. So here is the, the prediction on that. So I'm gonna say, well, through 2026, but also 2027, ads embedded in AI assistants are going to command structurally higher CPMs due to explicit user intent. This one y'all, yeah, going to be big. So, the key player here is obviously Chat GPT. They just started testing publicly testing ads, this week.
Jordan Wilson [00:20:06]:
So it's very fresh. But again, going into my background, right, I've I've been in, different MarTech comms, positions over the last twenty years, but I remember working on Google ads campaigns, what, six, like fifteen, sixteen years ago. What's going to be possible from a brand perspective. It is almost unfathomable. Right? I mean, we'll see. I think OpenAI, has a lot of work to do, to give the type of advertising tools that you have from, like, Google Ads. Right? But the level of intent that brands and advertisers will be able to tap into. Right? Because I mean, you spend big companies spend millions of dollars a month burning it just to learn more about who is clicking their ads and who is buying their products.
Jordan Wilson [00:21:12]:
Right. That is going to flip that is going to flip. And so what happens, you're not gonna have to, you know, show an ad to, a thousand people that you think are interested, just to get the 50 people that are interested. Right? Because it's gonna be completely different because now those 50 people, ChatGPT is gonna know instantly because people tell ChatGPT everything. Right? Everyone treats ChatGPT like their personal life coach, their therapist, their business advisor, all those things. You don't treat Google search like that. Right? You don't treat, Facebook like that or, Meta or whatever the kids are on this day. TikTok, Snapchat.
Jordan Wilson [00:21:59]:
Snapchat's still around. You share psychological intent with Edchat GPT. You share keywords with a Google search. And I think what that is going to be is a huge premium that companies are going to end up paying to chat GBT. I think in the, in the, or, or sorry, to open AI, I think in the early time, it's the CPMs. Right? The cost per milli or, right, essentially, the cost that you pay per impression. That's how, OpenAI is gonna be doing it first by impression, not through, you know, different attribution going all the way through and checking out and buying something. Although I think that they will offer that in the long run, they have to, I think, get some data on that first.
Jordan Wilson [00:22:47]:
But I think people are gonna end up paying early, maybe double of what you might be paying for, from other platforms just from an impressions standpoint. But I think in the long run, when they're able to, offer more than impressions, people are gonna pay a lot more, and they're gonna be happy with it. They're gonna be extremely happy with it because it is going to allow brands to go to market so much faster. Right? Startups, new companies, companies expanding into new markets with new service offerings. You're gonna be able to do it like that. You're not like you know, there's always been this, you know, advertising funnel. You have to have your, you know, your cold, you know, your cold, your prospecting, your medium, you know, your hot buyers. Right? It's not gonna be like that anymore.
Jordan Wilson [00:23:30]:
You're just gonna have buyers instantly, and you're not gonna have to build all these funnels because, well, ChatGPT is going to be the new funnel, and it is going to, command premium pricing. Number 18, multi Asian societies become the enterprise default architecture. So the prediction is by a 2026. Alright. Giving myself a little bit of time. Most serious AI deployments at the enterprise level are going to involve coordinated teams of specialized agents rather than just single, you you know, single assistance. So here's what it's gonna look like. One agent's gonna plan, another will execute, a third will verify.
Jordan Wilson [00:24:13]:
Right? And this essentially mirrors how human teams are already functioning. Right? And I think that's one of the reasons why, you know, agents didn't really take off in 2025 because people were looking at a single agent. But I think agents are going to be societies. Agents are going to be, sub agents like we talked about on yesterday's show. There's gonna be agent to agent commerce. There's gonna be agents that are negotiating with other agents without real human oversight. Right? But I do think that eventually, at least for, forward leaning enterprises, not saying every enterprise, but AI native forward leaning enterprises are going to be doing this. Right? So a user facing example might be something like, you know, build me a quarterly sales report, and then behind the scenes, the planner agent breaks it down.
Jordan Wilson [00:25:04]:
You You know, they grab your data from Salesforce, a chart from Tableau, you know, two paragraph summary. They assign subtasks, then the executor, you know, does them, then a verifier checks numbers before anything. You you know, then a a team of, design agents take it over from there. Then they say, okay. We need to design a deck, but in the deck, we need a couple charts. Let's get back to the, you know, the data visualization agents. Right? That's how it's going to be. It is going to be a society of agents that work together.
Jordan Wilson [00:25:37]:
And like I said on yesterday's show, it's going to be the, I think future of work is gonna be the burger. Humans are the buns where the front end, the back end, but the juicy stuff, the the the real meat of the work, is going to be the agents or society of agents. Right? We are the plain kind of boring bun. We give the tasks. We check it on the back end. We hold it together. Right? But the actual meat of what is going to get done, someone start using that. I just made that up, I think yesterday, and then I took it a little bit further today.
Jordan Wilson [00:26:11]:
Yeah. Can can can that be the next vibe coating? Right? Because it's I I don't know. The, the Asian burger. But that's what it's gonna be. And I think that you've already started to see this, right, if you've used, in the last week, because a lot of this is new. Right? If you've used, the new, Claude Opus 4.6, in Claude code, which, you know, I have doing now, that's what's happening already. Right? Sometimes the the the quote unquote agent you're talking to will do something on its own. Sometimes if you get a give it a much, more robust task, it's going to assign a team of agents.
Jordan Wilson [00:26:52]:
You know, one thing I'm playing around with right now is, how much, you know, how much leeway I have controlling those teams of sub agents. Right? I'm still, you know, trying to understand from a prompt engineering perspective. It's weird because I think prompt engineering, is starting to make a comeback strangely enough. Not right? Not like what I talked about, earlier about how context engineering is replacing, prompt engineering. I think that's just for, you know, work, you know, tax inside large language models. I don't think that's changing. But I think prompt engineering, at least when it comes to multi agent societies, it's gonna have a short lived, run here. Right? Because what I'm finding is, you know, some different prompting techniques, but, you know, some definite prompting techniques that, you know, were sticky in 2020 through 2023.
Jordan Wilson [00:27:43]:
You know, we might get some, added juice out of using those. Alright. So multi agent societies, enterprise default, no longer one agent. 19. This one, little spicy. Gotta take a drink. I think Microsoft is going to launch some sort of copilot works now reset campaign. So here is the prediction.
Jordan Wilson [00:28:09]:
So in 2026, Microsoft will publicly reposition Copilot around reliability and governance and real workflows, rather than just, you know, broad AI, you know, AI everywhere. So Copilot's early positioning, I think, centered on it being everywhere, like, everywhere. And I think the next phase is going to focus on performance in specific use cases. I think Microsoft's gonna highlight, you know, policy controls, audit logs, orchestration clarity, but I think they're actually going to do like in about face. Alright. I don't have any, insider intel on this, but I go back and think, you know, a a fun a fun example I used to talk talk about a little bit more, you know, when I did, did a little bit more, you know, brand work back in the day. Domino's. Right? One of the most, successful rebrands of all time.
Jordan Wilson [00:29:11]:
And what Domino's, did, they ran a very, famous commercial, where employees and executives, you know, they they essentially, you know, looked in on these focus groups and, you know, someone's like, oh, the pizza tastes like cardboard. Right? And Domino's paid millions of dollars to air these types of commercials everywhere that said their pizza tasted like cardboard. Right? And then they essentially it was a very public, about face. It was a public reset campaign. I don't think we're gonna see that level of it from Copilot, obviously, because that would cause, Microsoft to lose, I don't know, like, 500,000,000,000 in market cap overnight. But I do think we're gonna see a softer, but somewhat similar version of that, of, like, Copilot just works now. Microsoft knows, one of their biggest, climbs, and I think why a lot of enterprises, even if they're still paying, you know, Microsoft $3.65 Copilot seats, they're paying less. They're using it less utilization for a lot of companies I talk to is going down because it's getting easier and easier for enterprises to use ChatGPT enterprise to use right? A lot of people don't even realize this.
Jordan Wilson [00:30:36]:
Google Gemini has a completely separate business and enterprise product, and it's really good. Right? It's different than the Gemini. You know, many of us, they just came out with it, I don't know, like, six months ago. So it it's not like been there forever. It's it's new. You you know, Anthropic, they have a great enterprise product as well. I think Microsoft's been losing a lot of enterprise customers. They were obviously first.
Jordan Wilson [00:31:01]:
There was no other options. You know, it was the enterprise option or if you wanted, you know, you either had to, you you know, rag it, you know, build your build your pipeline, spend, you know, $6.07, 8 figures, or you had to say, alright. Well, we're just gonna go in this on you know, this consumer large language model because there was no choice really until 2025, you know, when OpenAI got serious about the enterprise, so did Claude. And then like I said, late in 2025 or sorry, 2024. And then in 2025, Google did as well. But we've seen a lot of reports, out of Microsoft. Right? Where its CEO, Sadia Nadella, reportedly said, like, hey. If we don't change, we're gonna get gobbled up.
Jordan Wilson [00:31:45]:
Right? One of the biggest companies in the world said, if we don't change, we're gonna get gobbled up. And, again, according to reports, the CEO, Sadia Nadella, is in a somewhat product management role, not actually. Right? But he's getting his hands on the Copilot product, which is not normal. It is not normal, for a, CEO, from one of the world's largest companies, to get involved in product, and he is, which is one of the reasons why I think Microsoft might come out with some sort of marketing messaging about how Copilot just works now. It's easier. Right? I've talked to literally countless, countless, countless, countless people. Even when I'm at Microsoft conferences, I'm always, you know, just a fly on the wall talking to people and, you know, so many people are just like, hey. Like, when Copilot works, it's great, but the majority of our people can't get it to work.
Jordan Wilson [00:32:43]:
They don't know where it is. They don't have access. Right? One thing about Chatt G B T, you know where it is. It's easy to get access. Right? You go to chattgbt.com. If you have the enterprise version, you have instant access to everything. Copilot is a little hard, right, because you can do the same thing in, like, nine different places. And you could complete the same task in nine different places, but your, team might only be in three of those places.
Jordan Wilson [00:33:13]:
And in, another three of those places, maybe you don't have access to all the folders that you do in other places. Right? It's it can be a a a a sticky spiderweb of access. So I do think Microsoft maybe simplifies, Copilot a little bit, in terms of usability and reliability, and just simplifies its messaging on it just works now. We'll see. Actually right? This is one of those, like, swing for the fences because no one's no one's saying this. It it is kind of random. But once I saw that story of Satya Nadella working on Copilot, I'm like, okay. How are they gonna respond? Something's going to change.
Jordan Wilson [00:33:51]:
We also know that Microsoft is, you know, working on their own models now now that, you know, their agreement with OpenAI has changed a little bit. So they're gonna be, pushing out their frontier models as well, which I think is only going to well, it's gonna help them in the long run. Obviously, Microsoft has so many other things going on than just Microsoft Copilot even on the AI side. But I do think that we are going to see some sort of, about face effort from Microsoft, essentially admitting without admitting that it's been hard to use Copilot. 20, the big four consultants consulting announces AI driven restructuring. So here is the prediction. In 2026, at least one big four firm publicly announces major restructuring, citing AI efficiency gains. So here's what I mean by major AI restructuring.
Jordan Wilson [00:34:43]:
We've already seen from big four consulting companies, and I called this last year layoffs and mass. Right? There's been multiple cases of 10,000 plus employee layoffs, at the big four over the past year or so. This is different. I think one of the big four consulting companies is going to essentially, similarly similarly, to how I, talked about Microsoft doing an about face. I think they're gonna publicly say knowledge work has changed, so we're changing too. And I actually have a fun, follow-up, to this one. Oh, man. I okay.
Jordan Wilson [00:35:22]:
I'm actually gonna skip ahead one and go back to '21. So I'm gonna go twenty twenty two twenty one. I should've had AI put this together because I I put one slide out of order. But I think that essentially, it's a pyramid model. Right? Not a pyramid scheme, but consulting is a pyramid model. Right? You hire junior associates. You charge you charge high rates for their grunt work, and then, well, agents are doing that grunt work and studies, and benchmarks show they do it better than junior associates. So eventually, companies that are spending millions of dollars and have spent millions of dollars on consulting, for the past, you know, decade, two decades, three decades, well, they're running these agents in house and and they're gonna be like, wait, why would we continue to pay our consulting company? Right? I got an email.
Jordan Wilson [00:36:19]:
I got an email the other day. I forgot the exact amount, you know, but someone said, you know, oh, I was, you know, gonna pay consulting company, you know, $80 for this project and, you know, accidentally did it myself. Not accidentally, but I did it myself in an hour, and the output was better than what they, you know, projected. Right? You're seeing stories like that all the time. Right? But when it's small little things, it doesn't matter. When it's big enterprise companies, it's going to matter. And I think consulting companies, I'm getting this on the record. You're gonna wanna be a first mover.
Jordan Wilson [00:36:56]:
Right? The rest of the world is gonna, you know, come to in terms of smart. If if if you know what you're doing, with agents now, you can one person can outperform an entire consulting team that doesn't have AI. Okay. So let me say this. Let me reframe this because you have to obviously know what you're talking about. So I'm gonna say a single consultant with AI can outperform a team of 20 consultants without AI. AI agents are that much of a multiplier, especially over the past two months. We need to wake up to this.
Jordan Wilson [00:37:37]:
This is why we've been seeing, you know, especially if you follow, you know, AI news and AI chatter. The last week or two, things have gotten a little weird and dark and gloomy, because I think people outside of that, you know, inner circle, right, even the, you know, some AI researchers today are like like just today today or this week, you know, we're getting stories of it. It's like, yeah. I don't think people, society understands what's happening and what impact this may have for jobs. Right. But you have to start where this is going to start is, high cost professional services, consulting, legal, finance, accounting, etcetera. Right. But these AI tools are going to compress delivery timelines from weeks to hours.
Jordan Wilson [00:38:29]:
And the end client is going to know. So I don't think the, the billable hour is going to die. But for, consultancies that still lead with the billing, billable hour, they're gonna lose. Right? It's gonna so much of what consultants do. Right? And and this isn't a knock on consultants. Right? But you read and ingest information. You synthesize it. You personalize it for, you know, your client, your market, your competitive, viewpoints, and then you create some output, some deliverable, right, a PowerPoint and spreadsheets.
Jordan Wilson [00:39:15]:
The models over the last three months do that. All of those steps in less than 1% of the time. Quality wise, can you one shot it? Right? I don't I don't know. If you have a former consultant at the keyboard, I think, yes. Right? The PowerPoints are the prettiest. But if you're using a design skill, they're way better than what a junior would do. They're way better than a template. So, yes, this is yeah.
Jordan Wilson [00:39:46]:
The consulting industry is going to get absolutely rocked, and one of the four is going to admit to it, and they're going to restructure, maybe not an entire company because we're talking, in some cases, hundreds of thousands of employees, but they are going to publicly restructure how they work, and they're gonna have to. Alright? And now I'm gonna skip from 20 to 22 because this is related. But we're gonna see professional services launch what I'm calling AI flanker brands. Alright. Here's here's what this is. So you have Verizon wireless. Right? Not cheap. And then you have, what is it? I think straight talk.
Jordan Wilson [00:40:27]:
Right. I have to, you know, AI mode this as, as I'm going live here. I believe it's it's that. Yes. So, straight talk is owned by Verizon, and it is just Verizon's services. Right? You are paying for the same thing. Right? But you're paying less on straight talk. It's essentially, you know, what you could call a flanker brand.
Jordan Wilson [00:40:55]:
And I think we're gonna see that from major professional services. So, the the the prediction here is by late twenty twenty six, major professional services firms are gonna introduce lower cost AI driven service lines to preempt the disruption that's coming. Think about it. Firms right now, they segment by complexity and risk. So lower tier services become semi automated and a premium advisory can still remain human heavy. And that's where I think the brand separation is gonna preserve the pricing power. But cannibalizing or the cannibalization is going to become a defensive strategy. Right? Companies are going to create essentially these little flanker brands.
Jordan Wilson [00:41:46]:
They're not gonna put their name on it just like Verizon doesn't, you know, necessarily slap their name on straight talk. They're like, here's our low cost offering. Right? We're not gonna give it the same marketing and maybe not the same customer service. Companies are gonna do this. I wouldn't be surprised if it's already happening. I haven't seen any stories of it. Right? And I'm I'm not saying it's going to be, you know, Deloitte or PWC or, you know, whatever the world's biggest law firm is, but it's going to be some of the bigger ones. They're going to do this because they're gonna have to, because these AI native brands for professional services, high priced professional services.
Jordan Wilson [00:42:25]:
I mean, we've already seen it. Look at, like, Harvey. Right? What they're doing in the legal side. There's a couple of them, you you know, doing it on the health side. They're going to be eating up the middle. Right? The big, big, they're untouchable. Right? These losses for the next year or two are gonna be a line item. The small, small, small ones, you know, they'll be fine because they're small enough to adjust, but the middle, they're gonna get eaten up by some of these flanker brands.
Jordan Wilson [00:42:54]:
So some of these companies are just going to have to create them. So it's kinda like a budget airline, you know, lower cost, semi automated, but it's like consulting light, but powered by AI. So I think clients are going to increasingly start demanding transparency because like I said, maybe in 2023, 2024, clients were none the wiser. And And then in 2025, maybe they started questioning like, hey. How are you using AI? Right? How is this affecting billable hours? Right? When I hired, you know, my attorney for everyday AI, you know, the first questions I asked the attorneys, how are you using AI, and how are you billing me for it? And then I gave them advice, and I'm like, okay. Well, that's good. That's not, right. But clients are gonna know.
Jordan Wilson [00:43:42]:
So you can no longer, you you know, charge, you know, $250,000, in research. Right? Oh, yeah. Yeah. Yeah. We gotta scope this project out and, you you know, become acclimated, you you know, with your industry and, you you know, do all these interviews and, reviews and, no. You know? An agent now does that, and it's gonna cost, you you know, $14. And it's gonna run for three days overnight, and then it's gonna deliver it all to you, all the answers. Right? Not having to waste that 95%.
Jordan Wilson [00:44:16]:
No. Here's the 5% that matters brought to you by an agent, and that costs you $14. Why are clients gonna continue to pay 250,000 for that? No. Someone's gonna say, alright. Well, we're gonna sell this for, you know, $500, and we're gonna give it to everyone. Right? And then these companies are gonna be like, okay. We need one of these companies. We need an AI native that takes our, IP, our expertise, repackage it, and get it out the door.
Jordan Wilson [00:44:49]:
Alright. Now I'm gonna go back one. Twenty one was vibe coding gets a rebrand. Yeah. Lot of rebranding here shifting around. It's gonna happen. I think it's gonna be called alright. I might not get this word right, this phrase, but maybe something like agentic software orchestration.
Jordan Wilson [00:45:05]:
But the the, prediction here is by, or sorry. In 2026, the enterprise language replaces the term vibe coding with something more structured. You know, might throw in the word, engineering in there instead instead of orchestration. Right? It might be agentic software engineering or something like that. Here's the reality. The AI labs, they're vibe coding to build the technology that we're all using. Even AI assisted coding, which I think is, you know, most, enterprises, would admit to that. Right? You know, they've probably been using, you know, GitHub Copilot from Microsoft, something like that.
Jordan Wilson [00:45:53]:
It's not gonna be like that anymore. Right? It's gonna be strictly vibe coding, but vibe coding doesn't have a good ring to it. Right? It implies this, you know, this informal, you know, facade that, you know, enterprises aren't gonna wanna tolerate that. Right? You're not gonna approve something that says, oh, we're gonna vibe code this. But that's essentially what's happening. Right? That's that's the reality. So I think what's gonna happen, is vibe coding is gonna grow up. Right? Kinda like how, you know, we used to think of startups, you you know, hoodies and and laptops and dorm rooms, but, no, now startups are billion dollar companies, with boardrooms and private equity and venture capital.
Jordan Wilson [00:46:37]:
Right? I think vibe coding is going to do the same thing, and this is gonna get called something else. But I think that, you know, the, the job of vibe coding is shifting from laying bricks to supervising the bricklaying machines. Right? Like I said, even right now, I'm technically vibe. I I I'm much more obviously in the, true vibe coding. Right? But if someone has software engineering experience, what they do is not vibe coding anymore. Right? Vibe coding is you're playing around, you're tinkering, and, oh, look at that. I have a working app. Cool.
Jordan Wilson [00:47:14]:
Agentic software orchestration is saying, okay. Here's the different platforms, the different tooling, the different scaffolding, the harnesses that that that we need, that our agents need, and and it's, you you know, gotta get done. Right? Right now, you know, vibe coding, it is still the the human duct tape. Right? Still pulling it together. Oh, let let me look at this. Why isn't this working? Let me feed it to document. You know? Let me tell it to go look up the solution. Right? I don't think agentic software orchestration is gonna be anything like that, but eventually, what they're gonna be doing, it's it's vibe coding in a suit and tie.
Jordan Wilson [00:47:51]:
Alright. '23, the slop debt crisis is going to make some large language model data unusable. So the prediction is by late twenty twenty six, at least one major lab will publicly acknowledge that part of its historical training data is too contaminated to trust. Alright. So this is a little bit different than model collapse, which a lot of people talk about. That's not what I'm talking about, this this cyclical regurgitation. I think, eventually, it's going to, come to fruition that so much of what's on the Internet right now is slop. Right.
Jordan Wilson [00:48:29]:
So what do I mean when I talk about slop debt? I think that there's going to be so much extra human time needed to weed out AI slop from training data. Right? So how training data works, the very oversimplified, maybe 95% accurate version, is, you know, essentially every single big AI lay lab, scrapes the Internet. They do have paid partnerships now. There's offline and online datasets, but everyone uses the same datasets, and they're extremely large. And there's good stuff in there. There's bad stuff in there. There's, you know, novel ideas and inventions waiting to be connected, and then there's, you you know, racist, homophobic, you you know, terrible things in there as well. So humans have to go through and take the bad stuff out.
Jordan Wilson [00:49:23]:
They have to train the model on, hey. When someone acts, you know, asks about x y z, here's what it means. Right? But now so much of that training data is slopped. Right? Back in, you you know, when OpenAI, you you know, in, you know, the early transformer days, 2015, 2016, 2017, when a lot of these models, the foundation of them was being built, there wasn't AI slop out there. Now there is, and it's bad, and it's inaccurate. That's the thing. When I'm saying AI slop, I'm not even saying, oh, look at all these m dashes and so many delves. Oh my gosh.
Jordan Wilson [00:49:59]:
We're diving in delves. No. I'm talking about information that is just not accurate, but it looks accurate. Right? It is misinformation and disinformation dressed up on a blog post on an enterprise website that no one has an idea. And I think it is going to cause, silent chaos, and I think that a big AI lab is gonna have to say, yeah. We have a problem here with this Internet thing. It is contaminated beyond repair. You know, it's like, you know, making a copy of a copy of a copy of a copy of something.
Jordan Wilson [00:50:33]:
Right? And then eventually, you can't see what it is, and someone just decided, oh, I'm gonna fill it in with marker, and no one checked it, and then they just keep copying. But knowing now it's not accurate anymore because someone just, filled it in. It's not even words anymore. It's alphabet soup jibber, gibberish. Alright. Three more. 24. A Frontier Lab is going to publicly declare humans rarely write code.
Jordan Wilson [00:50:57]:
Alright. Caveat on this one. But I do think that at least one major AI Lab executive states that AI now writes the majority of internal production code. So this is kind of already started to happen, but here's the thing. You know, Claude Code is a great example. The creator of Claude Code from Anthropic said, I believe, in December on Twitter that, you know, I think, you know, in that month that he wrote zero code. Okay? That's a difference between that's, you know, one individual, saying, hey. I don't write code anymore.
Jordan Wilson [00:51:34]:
That's one individual, you know, going into one product. You know, how many people at Anthropic are writing code? Thousands. Right? And I would still assume that there's a good overwhelming majority that are still writing some code from hand. Right? Maybe it's only 1%, 2%, you know, 20%. I don't know. But I'm good I'm guessing a majority of people even at the big AI labs are doing still a little bit by hand. I think by the end of the year, it's not gonna happen anymore. The jump that we saw from quarter three twenty twenty five to quarter four twenty twenty five in terms of what was capable, you know, not even just talking about SWEBench, terminal bench, all these, you know, coding benchmarks, but just what was possible with code now.
Jordan Wilson [00:52:23]:
It's it's weird. You know, you have software writing itself, you have models recursively learning, not just improving themselves, but writing themselves. That's what's happening now. All right. That's why things are starting to get a little weird. You know, we're, we're starting to get some, some, you know, glimpses, you know, some of our first official glimpses of, hey. This is groundwork for artificial superintelligence. Right? When models are improving themselves and humans are no longer hands on keyboard.
Jordan Wilson [00:52:55]:
Right? Humans are just like, cool. Good job, model. That's that's where we're getting. And I think that the statement when it comes is gonna be controversial. Engineers are gonna push back on the nuance. They're gonna say, oh, yes. But, you know, but CFOs and the enterprise are gonna amplify their messages, and competitors are gonna echo it as well. In enterprises, it's gonna take a little bit wild to follow suit, but at least one major AI lab is gonna say, we don't write code by hand anymore, which is gonna be wild, but I do think it's gonna happen.
Jordan Wilson [00:53:28]:
Alright. 25. Portable context engines are going to replace prompt libraries. Here was what I mean. In 2026, organizations are going to standardize portable versioned context engines that travel across AI tools. So think back to prompt libraries. Right? You might have a Chrome extension or something like that. Great one out there, you know, AIPRM, you know, that you can just plug into different large language models.
Jordan Wilson [00:53:57]:
Oh, here's some, you know, my saved prompts with placeholders. You know, you know me. I'm not a fan of saved prompts. However, prompt libraries prompt libraries are brittle. What I mean by that is they can break easily. One small update that and I'm not saying like, oh, going from GPT four to GPT five. No. I'm saying, do you know that g p t five instant was updated two days ago and it changed a lot? And that's the model that, hundreds of millions of people use.
Jordan Wilson [00:54:28]:
Did you know that, dear listener? No. Probably not. Unless you read our newsletter, then you did read it. Right? But if you had an entire prompt library, right, it could have not no longer work. Context engines, different. Context engines are modular, version controlled, and audible. This is how you, your team, your company, your industry works. Think of it as, like, you know, everything you, your company knows in markdown files.
Jordan Wilson [00:54:57]:
Right? But teams are gonna ship context updates like software releases. I'm already starting to do this. Right? I have my different markdown files for different things, and I'm updating them at all times. And whether I'm working in in quad code or I'm working, you know, something, you know, toying around with anti gravity from from Google or codex, I'm constantly keeping, these markdown files up to date. So I'm putting this into practice even though I'm, you know, part of a very small team. Right? I'm doing this myself, but, I think that the kind of the MCP, formalizes how these tools and contexts are gonna connect to the models. And that just does enable this kind of con this concept of a portable context engine. Right? Because, yes, I do advise companies to find your AI operating system of choice and move as many of your day to day processes in there.
Jordan Wilson [00:55:52]:
However, there's always gonna be instances where you're gonna be wanting to use multiple models for whatever reason. Alright. In our very last, here we go. Prediction 26. This is not technically about AGI. Right? Artificial general intelligence, and that's how I ended last year's show. And I don't care what you say. If you go back and look at definitions from 2010, 2011, 2012, 2013, twenty fourteen, twenty fifteen, we achieved AGI by far.
Jordan Wilson [00:56:24]:
Right? The thing is, over the past ten years, the definition of AGI has changed and it's been punted, right? We're not moving the goalposts on the definition of AGI. We're moving the entire football field. Right. It's changed so many times. There's very few dorks out there as dorky as me. I went back through for a show last year. I read hundreds of definitions of AGI from as early as 2020, right, using archive.org. We've already achieved the old definition, but that's beside the point here.
Jordan Wilson [00:57:01]:
Let me talk about GDP val. So GDP val is well short for GDP valued. So it's a benchmark created by OpenAI, and it's developed, to measure AI model performance on real world economically valuable tasks rather than, you know, academic or synthetic or blind taste test. Right? And this is quite literally giving models, economically valuable work, giving the same thing to humans. All right. So, and then you have expert judges and this is across, dozens of different sectors in The US and producing economically valuable work. Right? The consultants, example. Right? Go do research, you know, pull all this data, put it in a spreadsheet, put it in a report.
Jordan Wilson [00:57:53]:
Right? An an entire project or task front to back. A model does it, an expert human does it, and then a set of humans judge it, and they don't know whose is whose. Right? So right now, that is a GDP valve score. So the best models in the world are at 70%, and that means that either 70% of the time, the model either wins or ties. Right? How crazy is that? So I do think that we're gonna see 80%. Okay? And I think the win rate is actually going to increase a lot more. The tie rate is a little more right now. I think the win rate is going to go up even more than just the 10 percentage points.
Jordan Wilson [00:58:36]:
Right? That's the win tie rate. So this is so important and I think a good way to end, the show and to end our 2026 AI in prediction series. At this point last year, ma AI could not do this front to back. Right? You had to have a lot of duct tape, a lot of human duct tape. Now people don't realize this. They can do this scheduled. Right? Not having to, you know, go in and get this fancy agent and, you know, give it this memory and the scaffolding and connect. Right? No.
Jordan Wilson [00:59:17]:
Right? One model can go out schedule on its own. Right. So semi semi autonomously and do entire projects and create deliverables, right. Based on your context, based on your company's data. That's where we are. Right? So I think when you are making your roadmap for 2026, I want you to keep GDP valve in mind. Right? We're already at 70%. So that means right.
Jordan Wilson [00:59:57]:
AI model versus expert judged by experts model wins or ties against the human more often than not, which is, you know, why I sometimes simplify it and shorten it and say, well, AI is smarter than us better than us. There you go. A single AI model can do all of these things in one step in one shot with expert level outputs. So what does this mean for 2026? Right? I give you yes. Some of them, you know, are kind of fun, off the wall predictions, but I hope this is laying out a road map. My takeaway is this. There's no more waiting. There's no more waiting.
Jordan Wilson [01:00:51]:
Things right? I've always, for three years, every single day, try to tell you the truth in terms of AI can do this in terms of AI, can't do this. Sometimes I'm wrong. Sometimes I'm early. Sometimes I'm late, but that's why I do this. I put it all out there for you. And my takeaway here is this there's no more waiting right for you or your company companies that are still trying to reskill our, or upskill are going to die. Right? I'm not saying you're going to go out of business, but you're going to start a slow death. That's why since literally 2023, I've been telling you all to unlearn and rebuild.
Jordan Wilson [01:01:41]:
Okay. So if nothing else, right, even though some of these, predictions are off the wall and in fun, I'm not trying to end on a scary note. I am trying to say 2026 needs to be the year that you unlearn, tear it down and rebuild. The models are better than us. So we can't keep doing the same thing. That is a recipe for a disaster, and that is not the road map I am leaving you with. The road map, the plan ahead, don't travel down the same roads. Those roads lead to dead ends.
Jordan Wilson [01:02:24]:
We have to rebuild new roads. We all have to learn together how to travel from point a to point B. No one knows the best way yet, but we're gonna do it here together in 2026, and we're gonna do it every day on everyday AI. Alright. I hope this was helpful. Like I said, there was a lot more we didn't get to. Make sure you go listen to yesterday's episode. If this episode was helpful, if everyday AI is helpful to you, if you're still listening, my gosh, you literally owe it to yourself at this point to listen to me for an hour and three minutes.
Jordan Wilson [01:03:00]:
Go find the LinkedIn post. Go repost this. And I'm gonna send you the complete guide, 50 plus trends. I didn't get to everything. Right? Last year, I did five and a half hours worth of shows, you know, this year, a little shorter. So make sure you go repost this. It's a guide. It's ready to go.
Jordan Wilson [01:03:20]:
I already sent it to everyone, yesterday who reposted yesterday's show, so make sure you go do that. And then while you're at it, make sure if you haven't already, please go to your everydayai.com. Sign up for the free daily newsletter. Thanks for tuning in. Hope to see you back, well, next week and every day for more everyday AI. Thanks, y'all.
Midroll [01:03:40]:
And that's a wrap for today's edition of everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit your everydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers, and we'll see you next time.