Product1 distinct publisher3 min readPublished
Fast Company's three-bucket sort of proprietary, open weight and open source works best as a procurement checklist, because the middle bucket hands over the weights and keeps the training corpus out of sight.
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A request to take a workload off a frontier API and run a downloadable model on the company's own servers usually arrives costed and unlicensed [11]. It reads like an infrastructure decision, and it is signed by someone who will later be asked to put in writing what the model was trained on.
That is the part the download does not cover. Weights arrive as weights, and the corpus behind them generally does not come with them [13], which turns provenance from something a customer can inspect into something a customer repeats on the supplier's authority. The freedoms a licence reviewer actually cares about, the ones about modifying an artefact and passing it on, belong to the third category's definition [14], and a model can sit comfortably in the second category while offering none of them. "Open weight" names how the thing reaches you, and stops there.
The vocabulary gap is visible if you count the examples. In the text as supplied to us, the proprietary section names three model families, the open weight section names four models, and the open source section reaches its definition without naming any [15]. Seven named products across three labels, and the label that carries verification rights has none attached to it.
For procurement, two questions do more work than the three labels. Are the weights in your custody? Can you evidence rights and provenance without quoting the supplier? That is a four-cell grid, and the taxonomy supplies names for three of the cells [16]. Cell one is no custody with supplier attestation, the plug-the-workflow-into-an-API arrangement where the contract is your audit trail [6]. Cell two is custody without provenance, the self-hosted open weight deployment where contracts and business plans stay on your hardware [11] while the training data stays unknowable [13]. Cell three is custody and provenance together, the shape the Open Source Initiative definition describes [14]. Cell four has no name in the taxonomy and is the easiest one to land in by accident: a downloadable model consumed through somebody else's endpoint, where you hold neither the weights nor the paperwork.
Here is what teams tell themselves when they pick cell two: open means we control it. Here is what the deployment does: it moves the unanswerable question from where the data sits to where the model came from, and the second question is the one an auditor or an enterprise customer asks in writing.
The forcing function is three sentences you have to be able to finish without the phrase "the supplier says". Where these weights came from and who published them. What this licence permits for a fine-tune we ship to customers. What we can show about the training data when a customer's security review asks. If a sentence only completes with an attestation, the model behaves like cell one no matter what the download page called it, and it should be papered and priced as a supplier dependency rather than booked as something you own.
Ranked by verification strength, evidence, and original report placement.
Fast Company names as proprietary examples OpenAI's GPT and o-series that powers ChatGPT (citing GPT-5.6 and o3-mini), Anthropic's Claude, and Google's Gemini.
Open weight models can be significantly cheaper than proprietary LLMs on cost per token and are increasingly nearly as capable as the best frontier models, according to Fast Company.
Fast Company states that all large language models fall into one of three categories: proprietary, open weight, or open source.
Fast Company says open weight models are frequently confused with open source models, but that the two categories are distinct.
Proprietary LLMs are owned by a single entity, usually a large corporation; their code and training data are highly protected and largely a mystery to outsiders, and using them requires going through the cloud, according to Fast Company.
Most proprietary models are considered frontier models, seen as the most intelligent and capable, largely because of the computational power behind them and the volume of training data, which costs large amounts of money.
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1 article · August 29, 2026
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Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
Definitions borrowed and sound; examples unchecked
Two of the three definitions here are cited rather than reported — LM-Kit for what a weight is, the Open Source Initiative for what qualifies as open source, quoted at length — which is honest for an explainer and also the ceiling on what it can establish. The category boundaries survive scrutiny because they are conventional. The illustrations are shakier: naming GPT-5.6 and o3-mini as the live proprietary examples is a detail carried by Fast Company alone, and no second publisher has checked any of it.
Nothing here is counted
Not one number appears. "Nearly all" of China's best-known models being open weight, Llama standing as the American exception, open weights growing "increasingly nearly as capable" — these are characterisations, and Fast Company supplies no download figures, deployment cases, per-token prices or benchmark results to convert them into measurement. Rather than borrow an adoption level from the confident tone, we record none.
Slightly overstated, mostly in the cost pitch
The overreach is narrow and locatable. Calling open weights significantly cheaper and nearly frontier-grade, with no price and no benchmark, is the claim doing more work than its support. To Fast Company's credit, the piece immediately taxes its own pitch by pointing out that self-hosting means buying the machines. The subtler tidiness is architectural: presenting three buckets as covering every model makes a two-question decision — who holds the weights, and can the provenance be shown — look neater than it is, and quietly leaves the hosted-but-documented case without a name.
The boundary is drawn by a body invested in it
The most consequential line in the story — where open weight stops and open source begins — is drawn using the Open Source Initiative's criteria and then reinforced with an OSI quote arguing that full openness eases the burden of proving compliance. OSI exists to police that term, and the model list that follows is its own. Add a definitional metaphor sourced to LM-Kit, a vendor the reader is told nothing about, and an evergreen explainer format that rewards clean categories. None of this is disqualifying; it does mean the taxonomy is largely being narrated by parties with a stake in how it is drawn.
Firm on vocabulary, thin wherever numbers belong
We would repeat the distinction with confidence — it is stable, uncontested, and matches the criteria OSI publishes. We would not repeat anything specific without a second look: one publisher, no independent verification, a model version we could not corroborate, and our own tally of named examples per bucket turned out to be wrong once the open source section's list of OLMo, Pythia, T5, Amber and CrystalCoder is counted. Enough to reason with; not enough to procure on.