The OpenAI graveyard: All the deals and products that haven't happened

OpenAI’s Business Health and “Graveyard” Pattern

• Many see a pattern of splashy announcements (products, partnerships, megaprojects) that quietly die or are reversed once PR value is captured.
• Commenters describe OpenAI as “financially zombie-like,” heavily dependent on investor cash with no clear path to sustainable profits at its reported valuation.
• Some argue frequent product shutdowns and reversals erode trust and make OpenAI feel like Google at its worst, but without a proven ads cash cow.

Monetization, Ads, and Economics

• One camp believes LLM-based interfaces will eventually replace much of search and generate “hundreds of billions” in ad revenue, similar to Google/Meta.
• Skeptics counter: every attempt at LLM ads so far has been pulled back; users resist overt ads in work tools; and inference costs may dwarf ad income.
• Debate over whether consumers will tolerate ads if all major models adopt them; some argue users will flee to the least-annoying option, others say they’ll have no real choice.
• There is worry that making each “search” via LLM far more expensive may break the classic high-margin ad model.

Anthropic, Google, and Competitive Landscape

• Anthropic is widely perceived as more focused and “healthier,” with an enterprise-leaning strategy and fewer leadership dramas, but still very expensive and vulnerable if the bubble pops.
• Several argue Google/Gemini has structural advantages: ad machine, cloud, custom hardware, data, and existing enterprise relationships, making it a likely low-cost provider.

AI Bubble and Historical Analogies

• Many liken the current AI boom to past bubbles (dot-com, housing, railways, NFTs): transformative tech can still be massively overvalued.
• Comparisons to early PCs and the web: killer apps like spreadsheets and e-commerce productized and threw off cash faster than LLMs have so far.
• Some say markets can stay irrational for a long time; others think the hype/reality gap is now too large to sustain.

Technical Reality and User Experience

• Mixed experiences: some strongly prefer Claude for coding; others find it unreliable and favor GPT; some like Gemini’s value bundle.
• Many note LLMs still hallucinate and are poor at some factual or niche queries; they can be slower and less dependable than traditional search.
• Concerns that LLM-generated answers siphon traffic from websites, undermining the very content future models need—framed as a “tragedy of the commons.”

Experimentation vs. Lack of Focus

• One view: lots of failed launches are normal, even healthy, for a company still seeking product–market fit.
• Counterview: at an ~$800B+ valuation and enormous burn (with Sora’s daily loss figures disputed), this looks less like scrappy iteration and more like undisciplined “spaghetti on the wall.”
• Some see OpenAI as excellent at hype, fundraising, and politics, but much less proven at building durable, focused, profitable products.