10 thoughts about AI in 2026

2025 is a fever dream for AI. At the top end are dizzying financing and data center deals. Underlying that are companies of all shapes and sizes scrambling to build new products. Everyone has an AI marketing headline now. And all major AI labs produced substantial improvements to their models.

My favorite so far is Opus 4.5; I use it extensively to think and build software. It really complements my working style and is at its most compelling when I can use it to get myself unstuck. I alternate that with GPT 5.2 thinking, which is a slightly inferior model with tone issues¹. Gemini felt good when I tested it, but I didn’t find much reason to prefer it over the other two.

These days, I find myself in a privileged position where I use AI and build AI products full time, which affords me a unique vantage point. AI has changed my workflow in the last 3 years more so than the previous decade, and yet there’s a thrum of change ahead.

Here are ten not quite predictions for 2026.

1. The AI product that solves memory gets the market. For me, what stops the current crop of excellent models from feeling like AGI is the lack of robust memory. Forgetting is a difficult thing to get used to. Starting with a blank slate² in every language model interaction feels jarring in the face of their overwhelming reasoning prowess.
2. Coding agents will hit an improvement plateau. The current challenges in software engineering cannot be solved with only model improvements. The next step in engineering productivity will require the ability to inspect and use software environments. This is partly an ecosystem issue, and partly because key capabilities do not exist yet.
3. There will be a rise in tooling and advocacy for formal interface definitions. AI is already writing code at a speed that is difficult for a human to review thoroughly. If AI writing code can be thought of as capable but capricious, then the best way to harness that is to bind them to well-defined interfaces. As people stop spending time at the code level, we will start to see more time on schemas and contracts.
4. There will be a rethinking of how layers compose and route information beyond today’s transformer stack. It feels like both the transformer architecture and scaling laws are reaching the point of diminishing returns. If we are to see a leap in capabilities, for example, to better handle spatial reasoning, we will have to rethink the inputs and outputs of the model which would necessitate rethinking how the network is constructed.
5. New hardware optimizations inform model design. Prior models are in a way constrained by what GPUs can do. Given the amount of resources and scrutiny that AI labs are investing in chips, I would expect the new generation of models to be co-designed with hardware primitives.
6. Voice AI remains a niche. Voice AI has two major problems. First, as intelligent as the models are, AI is remarkably bad at reading social cues. Second, AI has a cadence problem. Speaking is a distinct domain from text and this is not simply adding a text-to-speech on top of generative models.
7. There will be emerging signs of personalized software. I have said this before: I believe we are at the end of the write once run everywhere economic model of software creation. In the world where code is cheap, a good use of the excess capacity is to build software in such a way that meets each user where they are.
8. Use cases that are popular in ChatGPT will unbundle. People often talk about chatbots as if they only regurgitate facts, but in practice they are enlisted to perform all manner of tasks like researching, writing documents, building websites, and so on. Coding agents have already spun off into their own thriving category, and more will follow.
9. There will be a new, popular UI paradigm for AI tools. This feels long overdue, and 2026 feels like the time. It is wild to me that in the three years since ChatGPT hit the market, the models have gone through multiple stepwise improvements, but the UI is essentially the same. Chat interfaces have a lot of virtues; they are a great fit for the language-based interactions that models require, but they are absolutely stressed by many of the use cases today.
10. Siri, powered by Gemini, will still disappoint. While I have no doubt that the Gemini model that Apple will use is capable, our expectations for what a good AI product is have risen over the past few years. Siri is situated in a different context, and as such a good model alone is insufficient to meet what the enthusiasts expect from Apple.