GPT-3 in the API: what a founder should do with it
OpenAI opened GPT-3 access through its API. A clear-headed look at what changes for business and where to slow down.
In late 2021 OpenAI opened the GPT-3 API to a broad audience. Until then access was gated, by application only. Now any team can connect, pay per token, and start building on top of a large language model.
I have already seen several projects that appeared within weeks of that opening. Text generators, support assistants, email drafting tools. The speed at which prototypes appear is genuinely impressive. But the question "what should we actually do with this" remains open for most businesses.
What GPT-3 is, in practical terms
GPT-3 is a language model trained to predict the next token in a piece of text. When you give it an instruction and some context, it generates a plausible continuation. It is not a search engine, not a knowledge base, and not a reasoning engine. It is a very well-trained text generator.
From this follow practical limits. The model knows nothing about your business data. It has no access to your systems. It does not remember previous sessions. It can write something confidently wrong - and that is called a hallucination.
Understanding this does not cancel the usefulness. But it does change which tasks the tool is right for.
Where it works right now
There are a few classes of tasks where GPT-3 delivers a real gain today, without complex infrastructure:
- Draft texts that a human edits afterwards: emails, descriptions, templates.
- Rephrasing and summarising existing material.
- Classifying and routing short texts - for instance, incoming support requests.
- Internal team tools where a mistake is not critical.
The common thread: a human in the loop, with final control staying with a person. Where a model error is expensive, additional verification is needed.
Where to slow down
I see a few traps that are easy to fall into during the euphoria of early prototypes.
The first is overconfidence in reliability. The model performs well on a test set and fails unpredictably on the real production stream, because the real stream differs from what you tested.
The second is underestimating the cost of maintenance. A prototype works while you are actively tending it. Turning it into a stable product is a separate engineering task.
The third is data as the bottleneck. If you want the model to know something specific about your domain, you have to either put that context in the prompt or fine-tune the model. Both paths require quality data that most companies do not have in ready shape.
Questions to answer before you start
Before deciding to build something on top of GPT-3, I recommend answering a few questions:
- What is the specific task? The more precise the definition, the easier it is to judge whether the tool fits.
- Who verifies the output? If no one does, the risk is too high.
- What happens when the model is wrong? What is the cost of an error?
- What does this look like in a year? Who maintains it, who pays for the API, who iterates?
- Do we have data that will make the output specific to our business?
If most of these questions have no answer, it is better to start with a narrower experiment - not a product.
What changes strategically
The most important shift is not the model's capabilities themselves. The most important shift is that the entry bar for experimenting with language models has dropped sharply. Previously this required a research team. Now it requires a developer and a credit card.
That means in the coming months many prototypes will appear, and some will become real tools. For a founder, the right reaction is neither panic nor euphoria. It is a calm understanding of where text automation creates real value in your business, and finding one concrete place to run a real experiment.