OpenAI, Anthropic IPO race: But what about all the AI risks?

There’s a particular kind of deja-vu setting across Silicon Valley right now for two of GenAI’s biggest poster childs. Going beyond pitch decks and late-stage funding rounds, this involves bankers hung up on adjectives like no one’s business. 

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Multiple reports suggest that OpenAI and Anthropic are getting into IPO conversations and want to go public within 2026 itself. This moment feels less like a victory lap and more like a race against gravity, if you think about it.

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The timing couldn’t be more awkward. Not because LLMs and GenAI lack utility – they clearly don’t – but because some of its most fundamental flaws are no longer footnotes. Hallucinations, for instance, aren’t just rounding errors that automatically fix themselves with scale (or RAG) – they’re a structural property of how these systems are trained and rewarded. Even OpenAI’s own research acknowledges this issue, even as it proposes a novel way to get around the problem. The subtext is clear, you don’t debug your way out of a worldview problem.

That wouldn’t matter as much if these systems stayed in low-stakes territory. But they haven’t, their areas of application are growing widely. Both OpenAI and Anthropic are now shipping health and medicine-adjacent tools – areas where confidence without correctness can be a serious liability. A 2025 Scientific Reports paper comparing large language models to physicians on clinical reasoning found models lagging experts, while still sounding impressively sure of themselves. That combination – wrong and confident – is the stuff malpractice lawyers dream about.

Then there’s summarization, the most enterprise-friendly use case of them all. Another peer-reviewed study in the Royal Society Open Science found that leading models were far more likely than humans to overgeneralize scientific findings. Five times more likely, in fact. If your chatbot is quietly inflating claims while “helping,” that’s another serious failure hiding in plain sight.

AI proponents often counter the hallucination argument with retrieval-augmented generation (or RAG0, sold as a kind of hallucination vaccine. But a Stanford-led legal reliability assessment poured cold water on that idea, showing that hallucinations persist even in supposedly closed, citation-heavy workflows. The risk actually increases when systems become harder to audit – exactly the kind enterprises prefer.

Agentic AI, the other pillar of the IPO narrative, fares no better under scrutiny either. Long-horizon planning remains a weak spot, to the point that researchers now routinely separate planning from execution because raw models simply can’t be trusted to hold state over time. If your business case hinges on autonomous agents running real processes, this is the seam skeptics tug at first.

None of this exists in a vacuum. As Reuters and The New York Times have reported, OpenAI and Anthropic’s IPO chatter is heating up amid ballooning valuations and growing regulatory attention. It’s at this precise moment that we need to call into question some of the unresolved fundamentals of GenAI, because they’re no longer just “research challenges” anymore, especially when public money will be at the stake.

OpenAI and Anthropic still offer real value. But IPOs are about narratives that can survive daylight. With Google and other incumbents closing the gap, the question isn’t whether generative AI works – it’s whether today’s leaders are rushing to cash in before the story gets harder to tell and sell.

Also read: MIT study says 95% of GenAI fails to deliver value: So why the layoffs?

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Jayesh Shinde

Executive Editor at Digit. Technology journalist since Jan 2008, with stints at Indiatimes.com and PCWorld.in. Enthusiastic dad, reluctant traveler, weekend gamer, LOTR nerd, pseudo bon vivant. View Full Profile