In the rush to cash in on the generative artificial intelligence gold rush, one possible outcome of AI’s future rarely gets discussed: what if the technology never works well enough to replace your co-workers, companies fail to use AI well or most AI startups simply fail?
Current estimates suggest big AI firms face a US$800 billion dollar revenue shortfall.
So far, genAI’s productivity gains are minimal and mostly for programmers and copywriters. GenAI does some neat, helpful things, but it’s not yet the engine of a new economy.
It’s not a bad future, but it’s different from the one currently driving news headlines. And it’s a future that doesn’t fit the narrative AI firms want to tell. Hype fuels new rounds of investment promising massive future profits.
Maybe genAI will turn out to be worthless, and maybe that’s fine.
Indispensable or indefensible?
Free genAI services, and cheap subscription services like ChatGPT and Gemini, cost a lot of money to run. Right now, however, there are growing questions about just how AI firms are going to make any money.
OpenAI CEO Sam Altman has been candid about how much money his firm spends, once quipping that every time ChatGPT says “please” or “thank you,” it costs the firms millions. Exactly how much OpenAI loses per chat is anyone’s guess, but Altman has also said even paid pro accounts lose money because of the high computing costs that come with each query.
Like many startups, genAI firms have followed the classic playbook: burn through money to attract and lock-in users with a killer product they can’t afford to miss out on. But most tech giants have not succeeded by creating high-cost products, but rather by making low-cost products users can’t quit, largely funded by advertising.
When companies try to find new value, the result is what journalist and author Cory Doctorow coined “enshittification,” or the gradual decline of platforms over time. In this case, enshittification means the number of ads increase to make up the loss of offering the free service.
OpenAI is considering bringing ads to ChatGPT, though the company says it is being “very thoughtful and tasteful” about how this is done.
It’s too soon to tell whether this playbook will work for genAI. There is a possibility that advertising might not generate enough revenue to justify the massive spending needed to power it. That is because genAI is becoming something of a liability.
The hidden costs of AI models
Another looming problem for genAI is copyright. Most AI firms are either being sued for using content without permission or entering costly contracts to license content.
GenAI has “learned” in a lot of dubious ways, including reading copyrighted books and scraping nearly anything said online. One model can recall “from memory” 42 per cent of the first Harry Potter novel.
Firms face a big financial headache of lobbying to exempt themselves from copyright woes and paying off publishers and creators to protect their models, which might end up a liability no matter what.
American AI startup Anthrophic tried to pay authors around US$3,000 dollars per book to train its models, adding up to proposed settlement that added up to US$1.5 billion dollars. But it was quickly thrown out by the courts for being too simple. Anthrophic’s current valuation of US$183 billion might get eaten up pretty quick in lawsuits.
The end result of all this is that AI is just too expensive to be owned, and is becoming something like a toxic asset: something that is useful but not valuable in and of itself.
Cheap or free genAI
Meta, perhaps strategically, has released its genAI model, Llama, as open source. Whether this was meant to upset its competitors or signal a different ethical stance, it means anyone with a decent computer can run their own local version of Llama for free.
Open AI models are another corporate strategy to lock in market share, with curious side effects. They are not as advanced as Gemini or ChatGPT, but they are good enough, and they are free (or at least cheaper than commercial models).
Open models upset the high valuations being placed on AI firms. Chinese firm DeepSeek momentarily tanked AI stocks when it released an open model that performed as well as the commercial models. DeepSeek’s motives are murky, but its success contributes to growing doubts about whether genAI is as valuable as assumed.
Open models — these by-products of industrial competition — are ubiquitous and getting easier to access. With enough success, commercial AI firms might be hard pressed to sell their services against free alternatives.
Investors could also become more skeptical of commercial AI, which could potentially dry up the taps of seed money. Even if open access models also end up being sued into oblivion, it will be much harder to remove them from the internet.
Can AI ever be owned?
The idea of genAI being worthless might recognise that knowledge is intangibly valuable. The best genAI models are trained off the world’s knowledge — so much information that the true price may be impossible to calculate.
Ironically, these efforts by AI firms to capture and commercialise the world’s knowledge might be the thing damning their products; a resource so valuable a price cannot be attached. These systems may be so indebted to collective intellectual labour such that their outputs cannot truly be owned.
If genAI can’t generate sustainable profits, the consequences will likely be mixed. Creators pursuing deals with AI firms may be out of luck; there will be no big cheques from OpenAI, Anthropic or Google if their models are liabilities.
Progress on genAI could stall, too, leaving consumers with “good enough” tools that are free to use. In that scenario, AI firms may become less important, the technology a little less powerful — and that might be perfectly OK. Users would still benefit from accessible, functional tools while being spared from another round of overhyped pitches doomed to fail.
The threat of AI being worth less than anticipated might be the best defence against the growing power of big tech today. If the business case for generative AI proves unsustainable, what better place for such an empire to crumble than on the balance sheets?
- is an Associate Professor in Information and Communication Technology Policy, Concordia University
- This article first appeared in The Conversation
