Nate B Jones outlines his AI predictions and optimism for 2026
A solo commentary by Nate B Jones on the trends and shifts he expects to define AI in 2026.
Summary
Nate B Jones of AI News & Strategy Daily delivers a forward-looking solo commentary on what he believes will define AI in 2026. His central argument is that the industry is exiting an era judged by hype — flashy benchmarks, exciting demos, and clever releases — and entering one judged by whether AI actually works in production. He contends that the most important developments of 2026 will not come from frontier models themselves, but from the ecosystem, tooling, and disciplined system design that surrounds them. He also flags robotics as a major area to watch, predicting rapid scaling of LLM-driven robotic capability built on a year of reinforcement learning groundwork laid in 2025.
Key Takeaways
FULL TRANSCRIPT
The shift from hype to results
Nate B Jones: I've been spending time this holiday season thinking about what I'm optimistic for in artificial intelligence and for all of us in the year ahead. And I think it comes down to this: I'm optimistic for 2026 and AI because we are exiting the era when AI is going to be judged by how clever the release is, how fancy the benchmark is, how exciting the demo is — and we are entering the era where it's going to be judged by whether it works.
I love that, because it means we're actually getting to a point this year where we can focus on delivering results with AI. That's hard, but it's meaningful work. The bubble of hype really burst in 2025. You felt it when ChatGPT 5 was disappointing to so many consumers. And the most instructive conversations I've had over the second half of the year especially have not focused on model roadmaps or benchmark charts. They've been about the critical edge cases and the driven work that shows up when you try to ship real systems — real multi-agent systems, real tool use systems, real systems that enable a human to do much, much more than they could do before.
So as we enter the new year, we're getting to a point where you can actually start to imagine the details needed to get an intelligence layer that we can all benefit from — something that helps our work go farther. For a lot of 2025, we were coloring in those gaps with hope because we couldn't imagine it. If you think back over the year: Claude Code is less than a year old, it was out in private beta in February. We had just had reasoning models at the start of 2025 and they were very new. Codex didn't exist until partway through 2025. Flux and Flux Pro both came out in 2025. All of these things that feel like essentials for the new systems in 2026 came into being over the course of the year and enabled us to start — I'd call it seeing in 4K. We're starting to see in high definition what's possible with these models in a way that we had to guess at before.
That's why a lot of my optimism for the new year is about the ecosystem around AI and not just about AI itself. And the optimism I have is also about the talent that goes with that ecosystem. One of the things I'm really excited to see is talent that can hold protocols and interfaces, technical details, verification loops, and the passion for the customer together — so that the technical reality and the job to be done sit in one head, not in two or three or four or five heads at a time. We're getting closer to those kinds of roles. We definitely have more development work to do on the people and org side so that more of those roles are published and available, but I see people who can do that emerging more and more, and they're incredibly valuable wherever they operate.
Protocols and process over prompting
So with that, let me get a little more detailed and talk about some bets I feel optimistic about as we head toward 2026.
One that I think is really interesting — and we don't talk about it a lot — is that I feel optimistic that our protocols and our process are going to start to matter even more than our prompting. We've been tempted to treat prompting as a very primary interface, and that was true in the chat era. Now I think we're going to start to treat it more as a layer in a more standardized toolchain for agentic workflows. The teams that win won't necessarily be the ones with the cleverest instructions. They'll be the ones where the systems can reliably call the tools, pass the structured outputs, hand off work between components, and reliably recover when something goes wrong.
That means in 2026, what I'm hopeful for is that we will be reinventing the wheel less. There'll be less bespoke glue holding everything together and more composable systems.
Taking constraints seriously
Another thing I'm optimistic for is the idea that we will take constraints seriously in AI. That sounds like a funny thing to be optimistic for, but it matters. The constraints are the difference between content and software. If you're just saying "write me 200 words" or "write me a story about X" or "help me with this prompt," you're really unconstrained and you're just asking for a chat response. But as we move more into agentic workflows, we're going to be giving our LLMs very tight constraints in order to enable them to do useful, repeatable work at scale.
That's why I'm saying we're moving through a transition from LLMs as content generators to LLMs as software. That's a really cool journey to see. Teams that start to take constraints seriously are going to get the layouts, the validation rules, the graceful degradation, the repair steps, the fallbacks — all of that baked in. And before they know it, their workflows are going to be in a spot where you can actually call it working software in production. That's going to enable a new class of AI-native experiences that go way beyond chat. We really have all the building blocks for that, and the only thing standing in the way is the discipline to start slotting LLMs in correctly.
Understanding where AI belongs in a workflow
Another one I'm excited about is really getting agentic workflows that understand where AI goes in those workflows. I think we've spent a lot of 2025 thinking that LLMs could do everything in the workflow. Where we're coming to at the end of the year is that LLMs are useful for very high-value roles that are narrowly scoped within agentic workflows — roles that have very specific deterministic transforms and checks associated with them, very specific tool calls. It's really about deciding and defining where that model is good at generating smart tokens and abstracting everything else away in the workflow so it doesn't have to do that.
We let the code do what the code's good at. We let it count. We let it route. We let it validate. We let it retry. We let it diff. We don't ask the LLM to do that in the prompt. Some people would say that's anti-agent, but to me that's very pro-agent. It's actually understanding what LLMs are good at and starting to build systems where they thrive. It's pro-reliability. I'm really excited to see teams start to pick that up.
Entropy reduction as a design principle
Another one I'm really interested in — this is going to sound theoretical, but we'll get practical here. I'm excited that teams are understanding how entropy works with LLM systems. In 2025, a lot of teams accidentally built systems that increase entropy and chaos. They had too many unconstrained steps, too many loops, too many opportunities for the model to get creative in the wrong place.
In 2026, I think those same builders are going to be the ones who start to understand that LLMs don't have to be drivers of entropy. People sometimes look at these token generators and say they're just uncontrolled, they're probabilistic, you can't manage them. One approach — which I talked about earlier — is to put business rules around it. But I actually think a higher-level approach is to look at LLMs as potentially entropy reducers. If you can structure where the LLM lives against your business outcomes correctly, then what was magical before can be a kind of disciplined magic now.
We're starting to see that in the chat-driven experiences off of ChatGPT and Claude in product. We're starting to see it in some of the AI-native interfaces. TL;Draw comes to mind — that definitely feels like magic but is actually extremely structured. Another example is the way Figma is handling AI at the end of 2025. Capsules is a good example. These are all places where LLMs are being harnessed in ways that produce more compelling, coherent, and beautifully designed experiences that on the whole decrease entropy.
There's less entropy in the system when I can get the answer I need inside the interface I have and I don't have to spray tokens everywhere finding some answer on the internet as a whole. There's less entropy in the system when I can talk to my Figma design and get it correctly laid out and then get it directly into Claude Code. Entropy is a very high-order way of talking about what we're doing when we design agentic systems. Teams are starting to recognize that you can design systems that are high entropy or low entropy depending on where and how you harness the LLM against a larger customer outcome.
My encouragement — the thing I'm excited about — is that teams are starting to intuitively grasp this even if they don't have the language. That means they're starting to recognize that LLMs need a lot of harnessing to produce beautiful experiences. But you can do that, and if you do, you can deliver things that are way beyond what ChatGPT brings you.
The post-ChatGPT software future and middleware
That brings me to another area where I'm optimistic. I think we are just at the beginning of a post-ChatGPT software future. One of the things I'm truly excited about is that Cursor has shown that even if you are a so-called wrapper, you can absolutely thrive in the middleware layer. That's a really interesting insight coming out of the year. And I think there's a lot of room to run, especially in non-technical areas, for middleware in 2026.
A lot of it comes down to what I've been talking about — designing good agentic systems, decreasing entropy, making it more beautiful and useful to the customer. One of the things that's really critical for that, which we're also starting to learn, is figuring out how to answer requests as if they're not all the same. ChatGPT trained us to answer requests as if they're all the same. But one of the characteristics of these new systems is they recognize that users have really different needs, and you can build different experiences around them.
If we talk about generative UI, generative UI is really downstream of the core insight that you can route users to experiences that matter to them outside the chatbot — in ways that are beautiful and useful. If I want to cancel my phone bill, I should be able to just get a generative UI pulled up and do that. I shouldn't have to go six clicks deep.
We're just at the beginning of figuring out how to map customer intent into what is probably a power law distribution of user utterances. You can start to say: 90% of my user utterances are very common, very usual — this is how I handle them. Then you use a great multi-agent workflow and generative UI to handle that long tail, and suddenly it becomes a really powerful experience that drives retention and engagement across the entire population.
Image-driven AI and the graphical world becoming normal
Another area where I'm really optimistic is what I would call the graphical AI world becoming really normal in 2026. I think this is a downstream breakthrough of Flux Pro. We're going to see a lot more work product that is just generated entirely as artifacts rather than prose. One very specific implication I think this has is that we will see slideware that is very normally just images now, because it's so easy to edit and regenerate images. You can already edit Flux images inside various tools and just regenerate — it's very trivial to get a new deck. When we live in that world where images are essentially solved, that opens up a lot of really interesting build opportunities in the new year around image-driven AI. We're just beginning to scratch the surface with that, but I'm really excited about it.
Dual-fluency talent repricing careers
Another one that's really interesting to me is that careers are repricing around dual fluency right now. The market is going to start to reward people who can do two things at once: one, understand how AI behaves at a high level of detail, and two, understand the underlying craft of their role and the customer.
Most organizations are still split right now between an AI person and a domain person that the AI person pairs with. I'm wondering if in 2026 we're going to start to see more roles that put them in the same head, because if you try to pair an AI person — even a very technical AI person — with a domain person, the head has only half the answers. Companies that can find those fully rounded people who understand a particular domain well and who also understand how AI behaves in high fidelity are going to be highly sought after. We're going to start to see HR systems rewrite jobs to get those people, because people are starting to recognize the value and the alpha in the market. They have a year under their belts with AI, they're training themselves, they're able to build things they weren't able to build before, and they can show their talent in a way that's really useful.
Robotics as the breakout story of 2026
The last thing I want to call out that I'm optimistic for is that I think robotics is going to have a huge year in 2026. I'm not really talking about humanoids only — I'm talking about robotics more broadly. We've had a year where we started to put in a lot of groundwork on reinforcement learning. Back in January of 2025, Nvidia announced their digital warehousing concept — this idea that you would give robots thousands of digital years of experience in simulated warehousing environments so that they would be safer in real warehousing environments. We've had a year to run on that.
Toward the end of this year, we had a breakthrough where we're now able to use personal POV cameras looking at hands to allow robots to infer how hands move and learn from human hand movements. The arc of the year is really around getting our learning in order so that in 2026 we can start to rapidly scale out LLM-driven robotic capability.
It's going to look like constrained environments at first. It's going to look like cheaper compute for deployment in designated areas of warehouses. There is absolutely going to be a big push on home robotics in 2026 — I don't know if that means we'll finally get the home robot laundry machine, but we'll see.
What I'm most interested in is that the winners in this space are going to be the ones that have the ability to reliably ship and update the brains of the robots they're shipping, so that consumers who are used to seeing LLM updates every two or three months don't feel left behind when their household robot is shipped to them in November and there's a new software drop in January. We're going to see ecosystems start to develop where people will say the robot primitives are all there — whether you're a business owner or a consumer who owns a robot at home — and what they want is over-the-air updates that ensure the robot's brain keeps getting smarter and it can use those fingers, or the pinchers, or whatever the robot has, more and more effectively over time. That's one of the pieces we have all the building blocks for, and I'm optimistic we'll get there in 2026.