OpenAI Did Not Have to Kill Custom GPTs (Why I Am Canceling My Subscription)

I am canceling my ChatGPT subscription, and the reason is narrow. For a long time, the one thing ChatGPT offered that Claude did not was the custom GPT, a bot anyone could configure in an afternoon and release to the world with a link. That advantage is about to vanish. Creation of new custom GPTs ends on October 26, 2026, and existing ones will stop working on December 11, 2026, after OpenAI announced the planned retirement on September 11, offering a migration path to plugins in their place.

The custom GPT was a modest technology with an outsized reach. You wrote instructions in plain language, attached a few files, tested the result, and shared a link. Anyone with a ChatGPT account could use it. No server, no code, no API key. A writing tutor that knows your syllabus, a statistics coach that refuses to hand over answers, a mock interviewer for job candidates. The scale of this was not trivial: when the GPT Store launched, it already held more than 3 million GPTs built by users. One of my own research projects runs on a custom GPT that scores tutoring transcripts. It now needs a new home.

Plugins are a different kind of object, and the difference matters more than the migration tool suggests. A custom GPT was a separate room. You walked in, and everything inside was shaped by its instructions. A plugin is an item in a toolbox that general ChatGPT reaches for when it sees fit. In the migration, a GPT's instructions turn into a Skill and its knowledge files become reference files. OpenAI itself warns that a migrated plugin may respond differently from the original GPT and advises testing the same prompts before finishing the move. For an educator, that caveat is the whole story. The value of a course bot lies in predictability. The student should meet the same tutor, with the same rules, every time. With a custom GPT, the instructions were the behavior. With a plugin, the instructions are one input among several, blended by a general model whose priorities the designer cannot see.

The more serious loss is sharing. Publishing a plugin publicly requires going through OpenAI's submission portal, and the developer guidelines describe a submission flow that includes review and approval. Inside organizations, workspace administrators decide whether members can share plugins or publish them to a workspace directory. For individuals, the picture is worse. Pro and personal-account users currently cannot share a Site-hosted plugin with other ChatGPT users through invitations or a share link. Observers have already noted that plugins have no equivalent to the old "Anyone with the link" setting, and migrated plugins start out private. In practice, a student who builds a study bot on Tuesday can no longer send it to classmates on Wednesday. She waits for a company to approve it, a company with little reason to prioritize a sophomore's flashcard tutor, or she goes somewhere else.

Somewhere else means one of two doors. The first is the API, which requires programming, hosting, and billing, the kind of technical knowledge most faculty and nearly all students lack. The second is a third-party platform that wraps the API in a friendly interface and charges for the privilege. Both doors shrink the circle of builders. The custom GPT democratized the making of AI tools. Its replacement quietly professionalizes it again.

Why would OpenAI do this? The official framing is consolidation: the company is shifting customization toward plugins that bundle reusable instructions, reference material, and connections to outside services. That logic may serve enterprise clients well. My guess, though, is that compute costs played a part. Public bots generate traffic OpenAI cannot predict, and a popular one can consume a great deal of capacity. That is a real problem with an obvious remedy: charge for it. Cap free usage, bill creators whose bots exceed a threshold, or require paid plans for heavy use. Any of these would keep the feature alive while aligning cost with consumption. Instead, OpenAI chose to solve a pricing problem with an architectural decision. That is a blunder, and the reaction among people who built on the platform suggests I am not alone in thinking so.

Higher education stands to lose the most. Building a bot is one of the best ways for students to understand how these systems actually work. Writing instructions teaches task decomposition. Testing teaches skepticism, because the bot will misbehave in ways its author did not anticipate. Sharing teaches responsibility, because other people will now rely on what you made. A student who builds a bot and gives it to classmates becomes an author rather than a consumer. The public link also made the classroom porous. A course project could leave the course and find users in the world, which is precisely the kind of authentic work we keep saying we want. A gated directory turns that open circulation into a corporate pipeline with a reviewer at the gate.

Companies often misjudge who their most valuable users are. The heavy individual user is a cost. The person who builds something on the platform and invites others in is a recruiter, an evangelist, and a teacher, all unpaid. Faculty who spent a semester refining a course bot, and students who learned by making one, were never traffic to be managed. They were the reason to pay. My subscription ends with the feature that justified it.

Innovation doesn’t need a faster engine

The doomsayers of AI are having their moment. They correctly point out that the rapid progress of large language models has slowed. Context windows remain limited, hallucinations persist, and bigger models no longer guarantee smarter ones. From this, they conclude that the age of AI breakthroughs is ending.

They are mistaking the engine for the journey.

History offers many parallels. When the internal combustion engine stopped getting dramatically better, innovation didn’t stop. That was when it really started. The real transformation came from everything built around it: road networks, trucking logistics, suburbs, the global supply chain. Likewise, the shipping container changed the world not through further improvements, but because it became the standard that reshaped ports, labor systems, and trade. When the core technology stabilizes, people finally start reimagining what to do with it.

This is the point we’ve reached with AI. The models are powerful, but most of their potential remains untouched. Businesses are still treating AI as a novelty, something to sprinkle on top of existing processes. Education systems, government workflows, healthcare administration; these are built as if nothing new has happened. We haven’t even begun to redesign for a world where everyone has a competent digital assistant.

The real question is not whether an AI can pass a medical exam. It’s how we organize diagnosis and care when every doctor has instant access to thousands of case studies. It’s not about whether an AI can draft an email. It’s about how office communication changes when routine writing takes seconds. The innovation now lies in application, not invention.

Limits are not the enemy. In fact, recognizing limits often helps creativity flourish. When designers accept that screen size on phones is fixed, they find smarter interfaces. We become inventive when the boundaries are clear. The same will happen with AI once we stop waiting for miracle upgrades and start asking better questions.

The real bottleneck is attention. Investment still flows heavily into training larger and larger models, chasing diminishing returns. Meanwhile, the tools that would actually change how people work or learn get far less support. It’s as if we are building faster trains while neglecting the tracks, stations, and maps.

There is a similar problem in education, where energy goes into protecting the structure of institutions while ignoring how learning could be improved. Just because we can do something well does not mean it is worth doing. And just because AI researchers can build a bigger model does not mean they should.

The most meaningful innovation is ready to happen. It is no longer about raw power, but about redesign. Once we shift our focus from models to uses, the next revolution begins.

The Start-up Advantage and the Plain Bot Paradox

In the gold rush to AI, start-ups seem, at first glance, to have the upper hand. They are unburdened by legacy infrastructure, free from the gravitational pull of yesterday’s systems, and unshackled by customer expectations formed in a pre-AI era. They can begin with a blank canvas and sketch directly in silicon, building products that assume AI not as an add-on, but as the core substrate. These AI-native approaches are unencumbered by the need to retrofit or translate—start-ups speak the native dialect of today’s machine learning systems, while incumbents struggle with costly accents.

In contrast, larger, established companies suffer from what could be called "retrofitting fatigue." Their products, honed over decades, rest on architectures that predate the transformer model. Introducing AI into such ecosystems isn’t like adding a module; it’s more akin to attempting a heart transplant on a marathon runner mid-race. Not only must the product work post-op, it must continue to serve a massive, often demanding, user base—an asset that is both their moat and their constraint.

Yet even as start-ups celebrate their greenfield momentum, they stumble into what we might call the plain bot paradox. No matter how clever the product, if the end-user can get equivalent value from a general-purpose AI like ChatGPT, what exactly is the start-up offering? The open secret in AI product development is this: it is easier than ever to build a “custom” bot that mimics almost any vertical-specific product. The problem is not technical feasibility. It’s differentiation.

A travel-planning bot? A productivity coach? A recruiter-screening assistant? All of these are delightful until a user realizes they can recreate something just as functional using a combination of ChatGPT and a few well-worded prompts. Or worse, that OpenAI or Anthropic might quietly roll out a built-in feature next week that wipes out an entire startup category—just as the “Learn with ChatGPT” feature recently did to a slew of bespoke AI tutoring tools. This isn’t disruption. It’s preemption.

The real kicker is that start-ups not only compete with each other but also with the very platforms they’re building on. This is like opening a coffee stand on a street where Starbucks has a legal right to install a kiosk next to you at any moment—and they already own the espresso machine.

So if start-ups risk commodification and incumbents risk inertia, is anyone safe? Some large companies attempt a third route: the internal start-up. Known in management lore as a “skunk works” team—originally a term coined at Lockheed to describe a renegade engineering group—these are designed to operate with the nimbleness of a start-up but the resources of a conglomerate. But even these in-house rebels face the plain bot paradox. They too must justify why their innovation can’t be replicated by a general AI and a plug-in. A sandboxed innovation team is still building castles on the same sand.

Which brings us to a more realistic and arguably wiser path forward for incumbents: don’t chase AI gimmicks, and certainly don’t just layer AI onto old products and call it transformation. (Microsoft, bless its heart, seems to be taking this route—slathering Copilot across its suite like a condiment, hoping it will make stale workflows taste fresh again.) Instead, the challenge is to imagine and invest in products that are both fundamentally new and fundamentally anchored in the company’s core assets—distribution, brand trust, proprietary data, deep domain expertise—things no plain bot can copy overnight.

For example, a bank doesn’t need to build yet another AI budgeting assistant. It needs to ask what role it can play in a world where money advice is free and instant. Perhaps the future product isn’t a dashboard, but a financial operating system deeply integrated with the bank’s own infrastructure—automated, secure, regulated, and impossible for a start-up to replicate without decades of licensing and customer trust.

In other words, companies must bet not on AI as a bolt-on feature, but on rethinking the problems they’re uniquely positioned to solve in an AI-saturated world. This might mean fewer moonshots and more thoughtful recalibrations. It might mean killing legacy products before customers are ready, or inventing new categories that make sense only if AI is taken for granted.

The trick, perhaps, is to act like a start-up but think like an incumbent. And for start-ups? To act like an incumbent long before they become one. Because in a world of rapidly generalizing intelligence, the question is not what can be built, but what can endure.