OpenAI mulls slashing prices as it competes with Anthropic for users
OpenAI’s reported plans to cut prices in response to Anthropic’s stronger high-end offerings are raising questions about how any major LLM provider will ever reach sustainable profitability. Commenters frame the move as part of a broader “race to the bottom,” with heavy losses, investor-subsidized tokens, and looming competition from much cheaper Chinese and open-source models that may erode proprietary moats. Many argue that in the long term, AI services will be commoditized and that success will hinge less on having the absolute best model and more on pricing, compliance, and viable enterprise-focused business models ahead of potential IPOs.
Profitability & Business Models
- Many doubt how frontier labs become profitable when training and infra dominate costs and models are sold below cost (“selling a dollar for 50 cents”).
- Some argue inference is profitable and R&D is the main drag; others say you can’t ever “stop training” without falling behind, unlike Amazon pausing expansion.
- Comparisons are made to Uber-style market-share subsidization, not early Amazon’s reinvested profits.
- Some think the real goal is a big IPO and equity payout rather than sustainable profits.
Price Cuts and Competitive Dynamics
- Users see a “race to the bottom”: cutting prices to gain share but deepening losses, risking a bubble and potential bankruptcy.
- Some think OpenAI is trying to drag Anthropic into a cash-burn war it can’t win.
- Others note Anthropic has raised prices on top models (e.g., Fable, Opus variants) even as cheaper competitors like DeepSeek cut prices.
Model Quality & Usage Experience
- Split views: some find Claude/Fable clearly superior for coding and long “agentic” tasks; others say Codex/GPT 5.5 are now better or at least “good enough.”
- Token limits are a recurring pain point, especially with Anthropic’s subscription + per-use model; some users never hit limits, others hit them frequently.
- Cheaper models like DeepSeek and Kimi are praised for ultra-low cost but criticized as slower, less reliable, and not yet safe to run unsupervised.
Enterprise vs Consumer Focus
- Many think Anthropic is more focused on pros/enterprise, while OpenAI chases both mass consumer and enterprise, seen as strategic diffusion.
- Debate over whether enterprise (pay-per-token, heavy usage) or consumer (subscriptions, huge user base) is ultimately more lucrative.
Local/Open Models & Commoditization
- Several expect LLMs to become a commodity, with many users eventually running “good enough” open models locally or on cheap rented GPUs.
- Chinese providers (DeepSeek, Qwen, GLM) are cited as existential price competitors, especially outside the US.
Ethics, Trust, and Company Perception
- A number of commenters avoid OpenAI over perceived warmongering, ad ambitions, or leadership hypocrisy and are happy to see it lose ground.
- Others argue they’ll choose tools based on capability and cost, but ethics can easily tip decisions when quality is “good enough.”
Bubble / Long-Term Outlook
- Some foresee an AI bubble akin to dotcom: LLMs survive, but current leaders may not (AOL/Yahoo analogy).
- Others think AI will settle as a must-have developer and cybersecurity tool, with expectations scaled back but industry enduring.