Your AI Partner Can Disappear Tomorrow. Then What?

Imagine you're Disney. You've put a billion dollars on the table. You've told your investors on live television that AI-generated videos are coming to Disney+. A three-year deal signed, 200 characters licensed, an entire creative pipeline built around a single AI product. Bob Iger is on CNBC calling it a chance to participate in a new form of media.

And then, on a Tuesday afternoon, you get a phone call. The product you built all of this on is gone. You have one hour before the world finds out.

That is not a hypothetical. That happened last week.

On March 24, 2026, OpenAI shut down Sora, its AI video generation app (Wall Street Journal, 2026). Six months after launch. The app had peaked at about a million users before collapsing to under 500,000 (Wall Street Journal, 2026). It was burning roughly $1 million a day in compute costs. Disney learned of the shutdown less than one hour before the public announcement (Wall Street Journal, 2026). The billion-dollar partnership died with it.

This is not a story about OpenAI making a bad product. This is a story about what happens when companies build load-bearing plans on AI platforms that haven't proven they can pay their own bills.

The Numbers Behind the Curtain

Here's a number that should stop you mid-scroll. Sora's total lifetime in-app revenue was reportedly $2.1 million (Megaone AI, 2026). The app was burning that amount every two days just to keep the servers running. Each 10-second clip reportedly cost about $1.30 to produce. A million users making free slop of Pikachu on a barbecue and Studio Ghibli knock-offs, all subsidized by venture capital.

The product looked like the future of creative AI. The balance sheet said otherwise.

OpenAI was also weeks away from training a new model code-named Spud, one that would power its enterprise and coding tools (Wall Street Journal, 2026). It needed GPU capacity. Every chip powering a Sora video was a chip not training the model that would actually generate revenue. With an IPO on the horizon and Anthropic eating its lunch on enterprise products, the math became simple: kill the spectacle, save the compute, chase the money.

And this is not an isolated case. In early 2025, Humane shut down after burning through $230 million on an AI wearable called the AI Pin. They sold the remains to HP for $116 million. The twist? Customers were told their devices, the physical gadgets they had purchased and held in their hands, would be remotely disabled once cloud services shut down (TechCrunch, 2025). Your product didn't just stop getting updates. It stopped working.

The question most people are asking is: why did Sora fail? That's the wrong question. The right question is: why did Disney, a company with some of the sharpest strategic minds on the planet, bet a billion dollars on a product that was six months old and losing money every day it ran?

Why Smart People Walk Into This Trap

Because the cost of not betting felt higher than the cost of betting wrong.

This is the part nobody wants to say out loud. The AI hype cycle creates a specific kind of pressure. Every board meeting, every investor call, every industry conference carries the same undercurrent: if you're not building with AI, you're falling behind. The fear of missing out is not just a consumer phenomenon. It drives billion-dollar corporate strategy.

Disney didn't stumble into the Sora deal blindly. Iger saw a chance to position Disney at the front of a generational technology shift. Licensing characters for AI video, integrating it into Disney+, storyboarding live-action remakes with AI tools. On paper, this was visionary.

In practice, it was a bet placed on a product that hadn't proven it could pay for itself.

And that's the trap. The AI pitch is always about what's possible. The due diligence question that nobody asks is simpler and less exciting: is this product economically viable? Not "is this technology impressive?" Not "does this demo well?" But: can this company afford to keep running this product at scale, without subsidy, for the duration of my contract?

Bole to, nobody asked the obvious question.

Product discontinuation is not a new phenomenon. Google has killed over 200 products in its history, including Google Reader, which had 129 million users when it died (KilledByGoogle.com). Adobe announced it was killing Animate, its 25-year-old animation software, in early 2026. The backlash was so fierce they reversed course within days, but only to "maintenance mode," a slower version of the same ending. Users said it would ruin their workflows. Adobe couldn't even recommend a full replacement from its own product line (TechCrunch, 2026).

But AI accelerates the cycle. Products launch, scale, and die in quarters, not years. The window between "this is the future" and "we're shutting it down" has compressed to the point where your planning cycle is longer than the product's lifespan.

If Disney's due diligence didn't catch this, what chance does your company have?

Why This Is Not Normal Vendor Risk

You might be thinking: vendors have always failed. Products have always been discontinued. Companies have always had to adapt. True. But AI platform dependency is a different animal, and the old risk playbooks don't cover it.

AI products move fast. Sora went from launch to death in six months. Traditional platforms evolve over years. Your planning cycle assumes stability. The AI market doesn't offer it.

AI tools go deep. They don't just sit in your tech stack. They embed into content pipelines, decision-making workflows, customer interactions. Pulling them out is not like swapping one SaaS app for another. It's like removing a wall after the building is finished and discovering it was load-bearing.

AI economics are opaque. You can look at Salesforce's public financials and assess whether the company is healthy. You cannot do that for most AI products. The unit economics are hidden behind subsidized pricing and venture capital. You have no way of knowing whether the AI product you're building on is profitable or burning through someone else's cash. And cash runs out.

Speed, depth, opacity. Most companies are not measuring this combination. The ones that are measuring it are still using frameworks designed for a world where vendors fail slowly and loudly. Not overnight and without warning.

How to Build on AI Without Building on Sand

So what do you do? You don't stop using AI. That would be as foolish as refusing to use cloud computing because a server once went down. The tools are real. The capabilities are real.

What's not real is the guarantee that any specific provider will be here, in its current form, a year from now.

Here's how to think about it.

Run the disappearance test. Before you go deep with any AI tool, ask one question: if this product shuts down next quarter, what breaks? If the answer is "nothing critical," you're fine. If the answer is "our content pipeline" or "our customer service workflow" or "our entire Disney+ short-form video strategy," that's a problem. Solve it now, not later.

Separate the capability from the provider. Video generation is real. Text generation is real. Code assistance is real. But the company providing it is a variable, not a constant. Build your workflow around what the technology does, with enough abstraction that you can swap providers without rebuilding from scratch.

Ask the uncomfortable economics question. Is this AI product profitable? If you can't answer that, and for most AI products right now you can't, you're building on someone else's burn rate. Sora was burning a million dollars a day while generating $2.1 million in total lifetime revenue. That is not a business model. That is a countdown.

Have an exit plan. A real one. Not a slide in a risk register. Not a bullet point in a vendor assessment that says "alternative providers identified." A tested plan that your team has actually walked through. What data do you need to export? What alternative tools have you evaluated? How long does migration take? If you can't answer those questions today, you'll be answering them in a panic when it matters most.

The Scaffolding Can Come Down

The AI tools are here. They're real, they're powerful, and companies that ignore them will fall behind. That's not the argument.

The argument is this: speed without stability is just velocity toward a wall.

Disney learned that last week. OpenAI itself learned it: you cannot burn a million dollars a day on spectacle while the competition eats your lunch on substance.

Build with AI. But build like someone who knows the scaffolding can come down. Ask the questions that nobody at that Disney board meeting asked. Know your exit before you need one.

Because if a company with a billion dollars of cushion got one hour's notice, you probably won't even get that.

References

KilledByGoogle.com. (n.d.). Killed by Google. https://killedbygoogle.com/

Megaone AI. (2026). Sora app revenue and download statistics. Megaone AI. [as cited in Outlook Respawn, March 2026]

TechCrunch. (2025, February 18). Humane's AI Pin is dead, as HP buys startup's assets for $116M. TechCrunch. https://techcrunch.com/2025/02/18/humanes-ai-pin-is-dead-as-hp-buys-startups-assets-for-116m/

TechCrunch. (2026, February 2). After backlash, Adobe cancels Adobe Animate shutdown and puts app on 'maintenance mode'. TechCrunch. https://techcrunch.com/2026/02/02/adobe-animate-is-shutting-down-as-company-focuses-on-ai/

TechCrunch. (2026, March 29). Why OpenAI really shut down Sora. TechCrunch. https://techcrunch.com/2026/03/29/why-openai-really-shut-down-sora/

Wall Street Journal. (2026, March). OpenAI shuts down Sora video platform. The Wall Street Journal. [as cited in TechCrunch, NBC News, and Variety, March 2026]