Examining the Risks of Generative AI

Published Date : 29/09/2025 

The generative AI market is facing significant challenges, including high operational costs, minimal productivity gains, and legal battles over copyright. This article explores the possibility that generative AI might not live up to the hype and what that means for the future of the technology. 

In the rush to cash in on the generative artificial intelligence (AI) 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 effectively, or most AI startups simply fail? Current estimates suggest big AI firms face a US$800 billion revenue shortfall. So far, generative AI’s (genAI) productivity gains are minimal and mostly benefit 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.

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 firm 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.

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 Anthropic tried to pay authors around US$3,000 dollars per book to train its models, adding up to a proposed settlement that added up to US$1.5 billion dollars. But it was quickly thrown out by the courts for being too simple. Anthropic’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.

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’s long-term viability. 

Frequently Asked Questions (FAQS): 

Q: What is generative AI?

A: Generative AI refers to artificial intelligence systems that can generate new content, such as text, images, and music, based on the data they have been trained on.

Q: Why are AI firms facing a revenue shortfall?

A: AI firms are facing a revenue shortfall due to high operational costs, minimal productivity gains, and the difficulty in monetizing their services through advertising or subscriptions.

Q: What are the legal challenges faced by AI firms?

A: AI firms are facing legal challenges primarily related to copyright infringement, as they often use copyrighted content to train their models without permission.

Q: How are open-source AI models affecting the market?

A: Open-source AI models, like Meta's Llama, are disrupting the market by providing free or low-cost alternatives to commercial AI services, potentially reducing the value of high-valued AI firms.

Q: What is the future of commercial AI firms?

A: The future of commercial AI firms is uncertain. They may struggle to compete with free or low-cost open-source models and face skepticism from investors if they cannot demonstrate long-term profitability. 

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