The state of AI in 2026 - part 1

"This tumultuous period of growth sees the IT industry focused on AI issues straddling everything from shadow AI to GPU hoarding to infrastructure overprovisioning to hallucinations, bias and so on. With foundation model training, inference and reasoning engines taking up many column inches, we're also concerned about the span now developing between large and small language models (medium models do exist, but they make fewer headlines) and we haven't even mentioned energy costs, compliance and real-time AI compute analysis at the Internet of Things edge."

"Almost all the progress over the last 18 months is underpinned by allowing an LLM to think at inference time (aka test time compute). This approach gives the model the time to compute and work through its reasoning processes as it scans over the 'mean' answer values it can deliver. Key research today is focused on how to get LLMs to maintain appropriate context over long-running tasks and outcomes. That could lead to self-learning and adaptation - something to watch out for in 2026."

AI is moving from adolescence toward maturity, with rapid market changes across model training, inference, and reasoning engines. IT industry concerns include shadow AI, GPU hoarding, infrastructure overprovisioning, hallucinations, and bias. Foundation model work emphasizes inference‑time computation that allows LLMs to spend compute on reasoning and to scan over mean answer values. Key research focuses on maintaining appropriate context across long‑running tasks to avoid context collapse and to enable potential self‑learning and adaptation. The growing span between large and small language models will shape deployment strategies, while energy costs, compliance, and edge compute constraints will drive infrastructure choices by 2026.

Read at Techzine Global