The glossary problem is now a delivery problem
On July 3, TechCrunch published The only AI glossary you’ll need this year, a reminder that the AI stack has become complicated enough to need its own operating language. That in itself is not new. What has changed is the cost of ambiguity.
Terms like AGI, LLMs, neural networks, deep learning, training, inference, and transfer learning no longer sit comfortably in the category of “background knowledge.” They show up in product reviews, model selection discussions, investor diligence, and launch plans. When those words mean slightly different things to different teams, the consequences are operational: slower decisions, muddier specs, mismatched expectations, and avoidable friction between the people building systems and the people funding or covering them.
That is why the interesting part of a living AI glossary is not the glossary itself. It is the fact that a glossary has started to behave like governance.
What the glossary is actually for
The TechCrunch piece frames the glossary as plain-English guidance for people who are “building with this stuff, investing in it, or just trying to keep up.” That scope matters. It signals that the document is not meant to be a vocabulary exercise for its own sake. It is meant to reduce translation overhead across technical and commercial workflows.
The core terms anchor the current conversation:
- AGI: still a nebulous label, but generally used for AI that can perform broadly across tasks at or above human level.
- LLMs: large language models, the systems most teams are now interacting with directly in products, internal tools, and APIs.
- Neural networks: the foundational architecture class behind many modern AI systems.
- Deep learning: the subset of machine learning built on multi-layer neural networks.
- Training vs. inference: a distinction that matters for cost, latency, deployment architecture, and product expectations.
- Transfer learning: a practical concept for adapting a model or learned representation to a new task.
Those definitions may look elementary, but in practice they are where many cross-functional conversations begin to drift. A product manager may talk about “training” when they mean fine-tuning. An investor may hear “LLM product” and assume a full-stack model business rather than an application layered on top of third-party infrastructure. An engineer may use “inference cost” precisely while a business stakeholder treats it as a loose proxy for cloud spend. A living glossary does not eliminate these gaps, but it narrows them before they harden into process failures.
Why living matters more than comprehensive
The key idea in TechCrunch’s framing is that the glossary is updated as the field evolves. That matters more than completeness because AI terminology does not stay still long enough to be frozen into a static reference.
New shorthand appears quickly. Old terms get overloaded. Marketing language seeps into technical conversations. A word that once had a narrow engineering meaning can start doing double duty in a pitch deck or a newsroom headline. By the time an organization writes an internal memo clarifying what it means by a term, the surrounding ecosystem may have already shifted again.
That is the tension at the center of glossary governance: the pressure to move fast versus the need to define terms carefully enough that fast-moving teams do not optimize against different assumptions.
Handled well, the glossary becomes a coordination layer. Handled poorly, it becomes another document no one trusts because it trails the market.
Turning definitions into governance
A glossary only becomes operational when it is tied to decisions.
The strongest version of this is not a wiki page buried in a knowledge base. It is a versioned, plain-English reference that is linked to actual product and engineering workflows. That means the glossary should influence how teams write requirements, evaluate vendors, plan experiments, and brief leadership.
A practical governance model usually includes four things:
- Versioning so teams know which definition is current.
- A review cadence so updates happen on a predictable schedule instead of ad hoc.
- Clear ownership so someone is responsible for maintaining definitions as the field changes.
- Workflow integration so glossary terms show up in roadmaps, model cards, launch checklists, and diligence materials.
This is where the plain-English part matters. If the glossary reads like a research paper, people will avoid it. If it reads like marketing copy, people will not trust it. The useful middle ground is precise but accessible: short definitions, explicit caveats, and enough context to make tradeoffs legible.
That structure can shorten product timelines. Teams spend less time re-litigating what a term means and more time deciding whether a capability is ready to ship, what it should be called in the UI, and what performance thresholds actually matter. It can also improve investment decisions by reducing category confusion. When everyone is using the same language for model architecture, deployment mode, and capability scope, it is easier to compare opportunities that would otherwise look similar in a pitch but diverge materially in execution.
Why standardization affects product and capital decisions
AI is now full of terms that sound like consensus but hide real disagreement. “AI-powered” tells you almost nothing. “Uses an LLM” can mean anything from prompt routing to proprietary model training. “On-device inference” carries implications for privacy, cost, and latency that are easy to miss if teams are not aligned on definitions. Even “transfer learning” can mean a disciplined adaptation strategy in one context and a vague shorthand for model reuse in another.
For product teams, this matters because roadmap decisions depend on what a capability actually requires. For engineering teams, it matters because architecture choices depend on whether a task is happening during training, at inference time, or through some hybrid workflow. For investors, it matters because the difference between infrastructure, tooling, and application layers is often embedded in those exact terms.
In other words, a glossary is not just about speaking clearly. It is about avoiding mispriced assumptions.
That does not mean language solves deployment risk. It does not. Model quality, data issues, safety controls, and operational constraints still have to be handled directly. But standardizing terminology removes a class of avoidable uncertainty that tends to slow diligence, complicate launch reviews, and create friction between technical and nontechnical decision-makers.
Five ways to adopt a living glossary inside a technical organization
If this sounds useful, the implementation should be modest and disciplined rather than grand.
- Assign one owner and a small review group
Pick a responsible editor from product, engineering, or technical operations, then add reviewers from legal, go-to-market, research, or investment teams as relevant. Ownership prevents the glossary from becoming everyone’s responsibility and therefore no one’s.
- Set a predictable update cadence
Review the glossary monthly or quarterly, depending on how fast the organization is shipping AI features. The point is not to change definitions constantly; it is to create a regular checkpoint so new terms and shifting usage are captured before they generate confusion.
- Tie definitions to actual decisions and metrics
Do not define terms in the abstract only. Link “inference” to latency or cost reporting, “training” to data and compute assumptions, and “transfer learning” to whether a team is adapting an existing model or building from scratch. A term becomes operational when it is connected to a measurable choice.
- Publish version history and change notes
If a definition changes, say what changed and why. That transparency is important for teams that rely on the glossary across roadmaps, diligence, compliance, and reporting. It also prevents people from treating the document as if it were static law.
- Collect cross-functional feedback before terms calcify
Ask engineering, product, sales, finance, and leadership where terms are being used inconsistently. The best signal often comes from the places where people are already having to translate between disciplines. Those friction points are exactly where the glossary can save time.
The larger implication
TechCrunch’s glossary is useful because it acknowledges something many AI teams have already learned the hard way: in a fast-changing market, language is part of the stack.
That does not make terminology glamorous. It makes it consequential. A shared glossary will not make a weak product strong, or a risky deployment safe, or a shaky investment sound. But it can improve the quality of the conversations that precede those decisions. And in a year when AI language is moving almost as quickly as AI tooling, that may be enough to matter.
