AI jargon used to arrive like library books. Now it spawns mid meeting, wearing a lanyard and asking for budget approval. TechCrunch’s updated AI glossary matters because the field is minting terms faster than teams can convert them into decisions, which is how you end up with six smart people confidently misunderstanding the same acronym. The newest vocabulary goblin is opaque recurrence, a term TechCrunch connects to OpenAI’s new Astra model. That makes this less a glossary story than a maintenance story. AI literacy is no longer a certificate you print once and abandon in a drawer next to your conference badge. For builders, investors, and product teams, terminology has become part of the operating environment, like API versions, eval suites, and the office espresso machine that only works if you negotiate with it. ## The glossary is doing incident response for language TechCrunch says its glossary is meant to explain the AI terms readers are most likely to encounter, including LLMs, RAG, RLHF, and opaque recurrence. The publication frames it for people building with AI, investing in it, or simply trying to keep up with coverage and podcasts. Crucially, TechCrunch describes the glossary as a regularly updated living document, which is the right format for a field where the dictionary keeps trying to become a changelog. The useful part is not that every reader becomes a transformer whisperer by lunch. It is that teams get a shared surface for conversation before a planning meeting turns into semantic dodgeball. When product managers, engineers, legal reviewers, and investors use the same term differently, the problem is not vocabulary trivia. It is requirements drift wearing a fake mustache. ## Opaque recurrence is not just vocabulary confetti TechCrunch identifies opaque recurrence as the reasoning technique in OpenAI’s new Astra model that has AI safety researchers rattled. A separate TechCrunch report is titled “OpenAI's new reasoning technique alarms AI safety experts,” which tells you this term is doing more work than decorating a keynote slide. It points to model behavior that people may need to discuss, monitor, and question, not just pronounce while looking thoughtful on a panel. That is the practical lesson. When a new term names a model behavior, teams should treat it like a prompt to revisit assumptions: what can be evaluated, what can be explained to users, what belongs in procurement questions, and what needs escalation. You do not need to pretend two words give you total understanding. That is how panel moderators are made. ## The AGI entry shows why definitions matter TechCrunch’s glossary also uses AGI to show how slippery AI language can be, noting that artificial general intelligence is a nebulous term. The entry cites OpenAI CEO Sam Altman describing AGI as the “equivalent of a median human that you could hire as a co-worker.” It also cites OpenAI’s charter defining AGI as “highly autonomous systems that outperform humans at most economically valuable work,” while Google DeepMind views AGI as “AI that’s at least as capable as humans at most cognitive tasks.” Those differences are not academic hair splitting. They shape how companies talk about capability, how investors hear ambition, and how product teams interpret risk. If AGI can mean several adjacent things depending on who is holding the microphone, then newer terms like opaque recurrence deserve even more careful handling. The glossary becomes a map of where consensus exists, where it wobbles, and where everyone is politely pretending the floor is stable. ## What readers should do next TechCrunch frames the glossary as something that will evolve with the field, and that is the habit readers should copy. Build a lightweight vocabulary review into product planning, investment diligence, technical docs, and safety conversations. If a term starts appearing in model coverage, do not wait for formal documentation to make it usable internally. Define it, attach it to decisions, and revise when better evidence arrives. For NewsPals readers, the takeaway is simple: AI literacy has become a moving target skill, not a museum tour. The teams that keep up will not be the ones memorizing every acronym for sport. They will be the ones turning new terms into better questions. The model may be opaque, but your team vocabulary does not get to be. ## Sources - Opaque recurrence, and other AI terms that you should probably know

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