Let's predict
This is my twenty-fourth and final post in the Advent of Writing series.
It’s been a good journey. For this last post, I want to end with a few deliberately cheesy predictions about AI and the state of tech in 2026.
Before that, an appeal to authority:
Prediction is very difficult, especially about the future. Niels Bohr
Predictions are easy to dismiss because they so often fail. When they do succeed, it frequently feels accidental, or correct for the wrong reasons. A random guess at best.
I think that misses the point. Predictions are rarely useful as literal forecasts. They are useful as tools to understand the present, and to surface what people believe about where things are heading.
A prediction, here and more generally, is an attempt to make sense of what is going on right now, and to sketch how those forces might continue, stall, or break.
With that framing in mind, here are a few predictions for 2026.
1. More specialized AI models
What does the present look like? So far, general-purpose LLMs have generated most of the revenue and driven the field forward. ChatGPT was the breakthrough that pulled AI out of a niche and into everyday conversation.
How will this continue? I expect general LLMs will remain the backbone of AI as most people understand it. But I expect smaller, more specialized models to become far more common.
The first hype wave has passed. Many companies are now confronting the actual cost of building and running LLM-based systems. That forces the question: can the same task be done with a smaller, cheaper, more focused model? And if a product cannot clearly demonstrate value or productivity gains, it becomes hard to justify anything beyond an MVP.
Audio models, embedding models, and other task-specific systems are tiny compared to general LLMs. I expect that logic to spread.
2. Inference will catch up to training
What does the present look like? Today, most GPUs are still used for training. Reliable numbers are hard to find, but estimates often suggest that 60-80% of GPU spend goes toward training rather than inference. That figure should be treated with skepticism, but it’s probably in the right ballpark.
This makes sense. We are still experimenting aggressively with architectures, scaling laws, and training techniques. New models are released constantly.
How will it continue? Training will remain important, and large investments will continue. But a rebalancing feels inevitable.
Once you spend enormous resources training a model, you want to deploy it widely and cheaply. We already see this pressure showing up. Google’s TPUs make long-context inference significantly cheaper for them than for competitors. Nvidia’s aquisition of Groq points in the same direction.
Inference is where models meet reality. That’s where cost, latency, and reliability start to matter more than benchmark scores.
3. 2026 won’t be revolutionary
What does the present look like? Since ChatGPT’s release in 2022, we’ve grown accustomed to model launches that feel revolutionary. It’s easy to forget how much progress has actually happened. ChatGPT was built on GPT-3.5, and today models like Llama 3 70B outperform it on most benchmarks.
The constant pace of “breakthroughs” has numbed us. If a new model doesn’t double some benchmark score, it barely registers online. This creates strong incentives to optimize for benchmarks rather than real-world usefulness, leading to impressive releases that sometimes disappoint in practice.
How will it continue? I expect impressive new models to keep coming, but with a noticeable plateau, especially for general-purpose LLMs.
At the same time, we still have a lot of work to do integrating existing models into the real world. Outside the AI bubble, adoption is slower and messier than it looks on Twitter/X. That gap won’t close through flashy releases, but through boring, friction-filled engineering and organizational change.
That work takes time.
These predictions are not meant as my future bragging rights. The point is not to post something today and return in a year to highlight the one thing that I happened to get correct.
I want to provoke thought. For you to reflect on what you believe is true about the world right now, and to ask how that trajectory might continue.
So think for yourself. What do you believe is happening today, and where do you think it leads?