Two years into the GPT era, and this community has seen a lot of hype, a lot of progress, and a lot of confusion. We’ve got GPT-5.2 in play with upgraded reasoning and execution capabilities. At the same time, agentic AI systems that don’t just respond to prompts but make decisions and perform tasks autonomously.
That raises a pragmatic question for developers who actually build real applications, not just speculative demos:
Is the customGPTs architecture still worth investing time in, or are we at a pivot point where agentic systems will eclipse GPT as the core abstraction?
You’re not talking about Generative Pretrained Transformers, the large language model technology? Because that’s what could be considered an architecture.
OpenAI reusing that name to make it more confusing after being denied a trademark by making it a ChatGPT sub-feature? There’s not much developer investment of time in making an “instructions” prompt and maybe providing OpenAI some free “actions” that you service. There’s millions of GPTs that have been created. I have a feeling it will continue for quite a while, as they are also offered to Enterprise and Business as a team share, alongside “projects”. They’ve been maintained, in that the GPT instructions are now in a “from a user” message, demoted, unable to control much except an intention. It is an “investment” with no return unless personally useful.
ChatGPT “apps” is not that and not a replacement - it is a few dozen top brand name partners being manually approved.
ChatGPT doesn’t offer “agentic” in being a custom-designed workflow of various appropriate models you select, running designed orchestrated sub-tasks for processing. So for developers who “actually build real applications”, ChatGPT is not where you build real applications. It’s where you chat with your computer buddy. Yet has a significant chunk of the world’s population as users.
Thanks for the thoughtful reply and the points you raised. To clear up the starting point for the casual reader, what I mean by “GPTs” in this context is specifically the custom GPTs feature in the OpenAI ecosystem the configured models that people build and (in my use case) want to ship as products to clients.
Because the same acronym gets used for both the model family and the product feature, it’s easy to conflate improvements at the model level with stagnation at the product/configuration level.
That distinction matters when you’re deciding where to invest development time. According to chatGpt (which found the link) enterprise adoption of structured workflows such as Projects and Custom GPTs has increased roughly 19× year-to-date, and these workflows now account for around 20 % of enterprise ChatGPT traffic, showing that many organizations are moving beyond ad-hoc prompts toward standardized, repeatable processes.
The real concern is whether the specific way custom GPTs are evolving (or not) gives you confidence that what you build now will remain supported, professional, and maintainable over the next year or two, especially given how fast the broader AI landscape is moving.
Moreover I specifically work as a consultant for small business, and budgets are limited, so the cost of investing time in a technology that may not have a clear roadmap or long-term support matters a lot in practical terms.