OpenAI and DeepSeek reasoning models trigger industry shift towards inference compute

Frontier artificial intelligence labs are changing how they build their most capable systems. After years spent pouring billions of pounds into ever-larger pre-training runs, the industry is pivoting toward inference-time compute: letting models spend seconds or minutes generating internal reasoning steps before spitting out an answer.

The shift gathered pace after OpenAI released its o-series reasoning models, followed closely by Chinese lab DeepSeek making its open-weights DeepSeek-R1 model available to the public. Both systems showed that applying reinforcement learning directly to chain-of-thought outputs allows models to crack complex mathematics, software engineering, and scientific reasoning tasks that previously tripped up standard foundation models.

For nearly five years, the recipe for better performance was simple: ingest more public internet text and run bigger graphics processing unit clusters. But that formula ran headlong into two practical problems. High-quality human text online is running out, and the power required to train individual frontier runs began straining regional electrical grids. Labs noticed diminishing returns from traditional pre-training alone.

What changes with test-time reasoning

Reasoning models do not merely predict the next token based on pattern matching. When asked to evaluate an intricate logic puzzle or debug complicated code, the model generates dozens of hidden intermediate steps. It tests assumptions, catches its own mistakes, backtracks, and refines its working before returning a final answer to the user.

Researchers call this test-time compute scaling. Instead of spending all compute budgets during the training months, developers spend it when the query is actually processed. A model given more time to think through a prompt consistently outperforms much larger models operating on single-pass answers.

DeepSeek's release of R1 intensified the pressure on proprietary developers. The Hangzhou-based firm claimed it trained its reasoning model using pure reinforcement learning without extensive supervised fine-tuning, publishing weights and architectural details openly. Independent researchers quickly reproduced parts of the methodology, proving that sophisticated reasoning capabilities were not the exclusive preserve of Silicon Valley tech giants with bottomless balance sheets.

Why it matters for AI agents

This mechanical change alters the outlook for autonomous software agents. Early attempts at AI agents broke down because large language models could not plan ahead. They hallucinated invalid file paths or made flawed assumptions early in a multi-step workflow and carried those errors through to the end.

With dependable reasoning, agentic systems can formulate a plan, run tests in an isolated software container, interpret the error messages, and alter course without human intervention. Benchmarks on real-world coding repositories show reasoning models completing multi-file software engineering tasks that older models could not finish.

What happens next

The race is now on to make reasoning cheaper and faster. Running hundreds of internal tokens for every user query makes inference expensive and slow, threatening business models that rely on cheap API tokens. Hardware makers and cloud providers are already restructuring data centres to prioritise low-latency memory and fast interconnects tailored for continuous generation.

Regulators and safety researchers also face fresh puzzles. Because chain-of-thought outputs can be monitored, developers gain a clearer window into how a model arrives at an answer. But as models learn to optimise their own intermediate thoughts to pass safety checks, checking whether a system is genuinely safe or merely gaming the evaluation will be the next major hurdle.