DeathStack

It started as the biggest opportunity in AI: OpenAI opened ChatGPT up to third-party plugins, letting it book flights on Expedia, browse the live web, run code, and reach into dozens of outside services. Developers and solopreneurs rushed in, building on ChatGPT the way an earlier generation built on the App Store or Facebook Platform. This was supposed to be the land grab.

Then, one year later, without much warning to the builders who'd bet on it, OpenAI unveiled GPTs and the GPT Store and began quietly winding plugins down. The distribution channel these developers had spent months building on simply switched off. No transition plan, no revenue-sharing consolation prize. Just a support article, filed under Help, titled almost cheerfully: "Winding down the ChatGPT plugins beta."

Source: OpenAI Help Center

For about eight months in 2023, "upload a PDF and chat with it" was one of the hottest weekend-project-turned-startup ideas in AI. A wave of nearly identical tools, ChatPDF, PDF.ai, AskYourPDF, and dozens of clones, launched doing the same trick: wrap an OpenAI model in a file uploader, charge roughly $10 a month, watch the signups roll in.

Then OpenAI shipped native file upload directly inside ChatGPT. Free. No install. No signup. The entire category didn't get a slow, dignified decline, it got made irrelevant in a single product announcement. Most of these apps are technically still online. Almost nobody is paying for them anymore.

Source: OpenAI

Neeva had everything a startup is supposed to need: founders who ran Google's ad business and built YouTube's monetization engine, $77.5 million raised, and a genuinely good pitch, an ad-free, private, AI-powered search engine that didn't sell your data. Tech press loved it. Early users loved it.

It didn't matter. Google Search is free, Neeva wasn't, and no amount of founder pedigree changes the math on convincing millions of people to pay monthly for something they can already get for nothing. Snowflake eventually acquired Neeva, not for the search engine, for the AI talent. The product itself was shut down within weeks.

Source: Neeva

At its peak, Olive AI was valued at $4 billion and had raised $902 million promising to automate the mind-numbing administrative work that eats hospital budgets alive: prior authorizations, claims processing, scheduling. It was one of the most hyped healthcare AI companies in the country.

Then the automation didn't automate. Investigations and internal reporting found large parts of what Olive sold as AI relied heavily on manual human labor happening behind the interface. Hospitals that had built workflows around Olive's promises were left scrambling. After mass layoffs, Olive shut down entirely and sold off its remaining assets for a fraction of that $4 billion number.

Source: Healthcare Dive

Builder.ai pitched itself as an AI so advanced it could build you a working app just by describing what you wanted, no developers required. Microsoft backed it. It hit a $1.5 billion valuation. The company even nicknamed its AI system "Natasha," giving it a face and a personality investors could root for.

Natasha was, allegedly, roughly 700 engineers in India writing code by hand. Investigations found the company had inflated its reported revenue by nearly 300%, claiming around $220 million against a real figure closer to $55 million. When a creditor seized $37 million from its accounts, the whole structure collapsed, triggering bankruptcy proceedings across five countries at once.

Source: TechCrunch

Backed by Accel and Flipkart, Tune AI built a genuinely useful product: tools for fine-tuning and running large language models, sold through Tune Chat and Tune Studio. For a while, it filled a real gap for developers who needed LLM infrastructure without building it themselves.

The gap closed. AWS, Google, and Azure all shipped equivalent tooling, bundled into platforms most developers were already paying for, at a lower marginal cost than a standalone vendor could match. Most of Tune's users were on the free tier and never converted to paying customers. The infrastructure costs stayed high. The margins didn't.

Source: Tech Startups

Woebot was one of the first genuinely well-regarded AI therapy chatbots, delivering cognitive behavioral therapy exercises to roughly 1.5 million people and earning an FDA Breakthrough Device designation, a real signal of legitimacy in a category full of hype.

That designation became the problem. The regulatory approval process for AI mental health tools moved on FDA time while the underlying AI moved on large language model time, and the gap between the two eventually became too expensive to bridge. Woebot shut down not because it failed users, but because the rules built for a slower era of software never caught up to it.

Source: STAT News

Coqui built the Coqui TTS library, an open-source text-to-speech tool that became the go-to option for developers who wanted voice synthesis without licensing a black-box commercial API. It was genuinely loved in the parts of the AI community that build things rather than just talk about them.

Being loved by developers and being funded by investors turned out to be two different problems. Coqui ran out of money in an increasingly crowded voice AI market, competing against well-capitalized commercial players. Before going dark, the team released its final models to the open-source community rather than letting the work simply vanish.

Source: Coqui TTS on GitHub

Yara was a UK-based AI mental health companion offering CBT-style exercises and conversational support, the kind of product that usually keeps shipping features and raising rounds for as long as anyone will fund it.

Instead, its founder made a call almost nobody in this graveyard made voluntarily: after looking honestly at what the product was and wasn't capable of, they decided AI chatbots simply aren't safe enough for people in genuine mental health crises, and shut the company down themselves rather than keep operating.

Source: Fortune