Three years ago, running AI in production required enterprise budgets and dedicated ML (machine learning) engineers. The picture IEEE Spectrum paints in their State of AI 2026 analysis looks different: AI has moved into the baseline of how businesses operate, costs have dropped sharply, and technology that was out of reach for most people in 2023 is now background infrastructure.
The Inference Cost Story
The most consequential shift in AI over the past two years is the collapse in inference costs - inference meaning what happens when you actually use an AI model to answer a question, as opposed to the months-long process of training it in the first place. Running a query through a capable AI model in 2026 costs a fraction of what it did in 2023. That reduction makes AI tools viable at price points that would have been impossible two years ago.
For practitioners, this changes the calculation on what tools are worth paying for. When the underlying cost to run a model drops, the tools built on top of those models either get cheaper or get better margins. Either outcome is better for users than the alternative.
Open Source Closed the Gap
One of the more significant findings in any 2026 AI analysis is how quickly open-source models - models whose code and weights are publicly available, meaning anyone can download and run them - closed the gap on proprietary leaders like ChatGPT. Meta's Llama series, Mistral, and several other open-weight models are now competitive with commercial offerings on most standard benchmarks for reasoning, writing, and instruction-following.
That matters practically because it expands the options for businesses that want to run AI on their own servers without sending data to a third-party provider. It also creates real competitive pressure on proprietary model pricing in a way that simply didn't exist eighteen months ago.
Where Adoption Is Real
Adoption numbers need context. Usage is high in content creation, coding assistance, and customer-facing chatbots. It's moving more slowly in legal, finance, and healthcare - not because the tools are bad, but because the acceptable error rate in those fields is far lower. A model confidently generating a wrong answer (called a hallucination) is a manageable annoyance in a blog draft. It's a material problem in a contract clause or a diagnostic summary.
Energy consumption is the counterweight to the cost story. Data center power demand from AI workloads has grown substantially, and that growth is showing up in infrastructure investment decisions and policy discussions that will affect AI pricing over the next several years. The cost of running a query went down. The cost of the electricity needed to run it is going up.