AI Glossary Explains Opaque Recurrence and AGI

AI is changing how the world works while simultaneously creating the very language used to describe it. In product meetings, investor briefings, and technical discussions, terms like LLM, RAG, and RLHF fly around. According to reporting by TechCrunch AI’s Natasha Lomas, Romain Dillet, Kyle Wiggers, and Lucas Ropek, this rapid pace of change is leaving even experts feeling bewildered. In a glossary published on September 7, 2026, the outlet organized definitions of key terms in plain English. This article is based on TechCrunch AI (All Rights Reserved). It relies on fair quotation under Article 32 of the Japanese Copyright Act.

The vocabulary moves fast enough to make even very smart people in the tech world feel a little insecure.

The sentence above succinctly captures how it feels on the ground. The glossary is intended to be a living document, updated regularly. For Japanese developers and planners as well, grasping this common language is a prerequisite for implementation and procurement.

The Reality on the Development Floor as AI

Terms Proliferate

With large language models at the core of products, feature descriptions have become more abstract. Reasoning, tuning, search integration, and delegated execution now coexist on a single screen. Cases where the same term is used with different meanings in sales materials and design documents have also increased. Such terminological gaps cause rework in requirements definition and delays in safety reviews. Having shared definitions is a factor that determines development speed and quality. Especially in contracted development and joint research, agreement on terms is a precondition for contracts. The glossary can be seen as a practical tool for reducing such friction.

The scope of application goes beyond language processing. In video generation, AI Video Generation MoneyPrinterTurbo Surges on GitHub has drawn attention. In reading devices, Open-Source eReader Open Book Touch Launches Crowdfunding has become a talking point. In display technology, LG OLED evo, Creator Original Picture Mode with Prime Video was announced. AI features are combining with devices and video infrastructure, spreading across entire products. Understanding the terminology helps grasp not just individual models but entire product systems.

What Opaque Recurrence Shows About Changing

Reasoning

Opaque recurrence was featured as a term that emerged last week. It is said to refer to a reasoning technique in OpenAI’s new Astra. It is described as raising alarm among safety researchers. Specific mechanisms and evaluation results are not presented within the scope of this summary. The name suggests the opacity of recursive processing. Reasoning that is difficult to verify externally becomes a challenge for audits and accountability. At this point, it is best viewed as a signpost indicating where issues requiring attention lie.

Visualizing the reasoning process has become a focus for both safety and performance. If intermediate steps can be recorded, tracing the cause of errors becomes easier. Without such records, ensuring reproducibility becomes difficult. In enterprise adoption, there are cases where how records are stored becomes a procurement condition. On the research side, standardization of evaluation methods is being called for. The debate around opaque recurrence can be placed within this trend. Disclosure of detailed technical information will likely shape future understanding.

Three Diverging Views on the Definition of AGI

AGI, which stands for artificial general intelligence, is a term without a settled definition. OpenAI CEO Sam Altman has said it is equivalent to an average human hireable as a co-worker. The company’s charter defines it as a highly autonomous system that outperforms humans at most economically valuable work. Google DeepMind views it as AI that matches humans at many cognitive tasks. The three differ in how they set the level and scope of capability. Interpretations are said to differ even at the forefront of research. Differences in definition directly lead to differences in goal-setting and progress assessment.

Differences in definition also affect product strategy. If replacing humans is emphasized, automation rates and cost-effectiveness become the metrics. If cognitive ability is emphasized, the design of evaluation tasks becomes the metric. In explanations to investors, claims about timing of arrival change. In regulatory debates, where to draw the line on scope changes. It is risky to proceed with discussion on the assumption of a single definition. There is a need to make it a habit to specify which version of AGI is meant.

Unpacking AI Agents and API Integration

An AI agent refers to a tool that performs a series of tasks on behalf of the user. Expense reporting, booking, securing tables, and creating and maintaining code were cited as examples. Its defining feature is that it goes beyond simple conversational responses to execute multi-step processes. In some cases, it operates by marshaling multiple AI systems. However, the meaning is not fixed, and its scope varies depending on standpoint. Infrastructure is also still under development, and the envisioned capabilities are yet to be realized. It is reasonable to understand it as a concept for autonomously handling multi-step work.

An API endpoint is an interface for operations behind the scenes. Other programs call it to retrieve data and execute operations. Many home devices and connectivity platforms have interfaces invisible to users. Developers combine interfaces to build integrations with other applications. Agents are strengthening their ability to discover interfaces on their own and operate them directly. While enabling powerful automation, unexpected behaviors can also occur. The scope of interface exposure and permission design are seen as central to safety management.

How Chain of Thought Improves Reasoning Accuracy

Chain of thought refers to a method of thinking step by step. Humans can answer simple questions immediately. As an example, the question of which is taller, a giraffe or a cat, was cited. For complex questions, proceeding step by step increases accuracy. There is a method in which models are made to generate intermediate steps to reach a conclusion. It is applied to reading design documents and checking calculation procedures. It is regarded as a practical technique for improving the quality of reasoning.

The spread of the technique has prompted changes in evaluation and operations. Leaving intermediate processes makes it easier to pinpoint errors. Because it consumes more computing resources, cost structures need to be reviewed. In safety reviews, checking intermediate records provides clues. In education, it is applied as a way of presenting solutions. It is seen not merely as a performance improvement measure but as a concept affecting operations as a whole.

Why Keeping the Glossary as a Living

Document Matters

AI vocabulary changes along with model updates. When new reasoning techniques or tuning methods appear, definitions need to be added. There are also cases where old terms change meaning. If a document created once is fixed, it will soon diverge from reality. A document design premised on regular updates is appropriate. TechCrunch AI’s glossary also adopts that policy. The system for tracking change itself is seen as part of technological understanding.

For Japanese companies, the challenge is how to translate terms from English-speaking contexts. Literal translations may not convey meaning and can cause confusion on the ground. Developing translation tables and accumulating usage examples will determine success or failure of adoption. In design documents and contracts, it is effective to spell out the full forms of abbreviations. In training materials, explanations tied to specific operation screens are effective. Terminology management is regarded as an activity directly linked to quality control.

Editorial Opinion

On short-term impact: Over the next three to six months, explicit definitions of terms in procurement documents are expected to spread. Terms pointing to opacity in reasoning, such as opaque recurrence, are expected to be incorporated into safety review checklists. In agent procurement, descriptions of API operation scope will become more detailed, and reviews of permission design will advance.

On the long-term view: Over the next one to three years, differing definitions of AGI are expected to create divergence in evaluation systems. This is because if capability criteria differ, the yardsticks for performance comparison also change. Intermediate records from chain of thought will become established as audit trails, with practices for retention and disclosure taking shape. Terminology management is expected to become a quality foundation in both education and practice.

Questions from the editors: How much opacity in the reasoning process should be tolerated? Should convenience or verifiability take priority? If the market expands while definitions remain divided, who should maintain the common language?

References

• “Opaque recurrence, and other AI terms that you should probably know”, by Natasha Lomas, Romain Dillet, Kyle Wiggers, Lucas Ropek — TechCrunch AI, 2026-09-07T19:24:00.000Z (ARR)
• Source URL: https://techcrunch.com/2026/09/07/artificial-intelligence-definition-glossary-hallucinations-guide-to-common-ai-terms/