Yesterday, my free year of Perplexity Pro expired.
It was fine. Decent research tool for generic “thought leadership” drawing from online sources. Bad for academic research in fields where many of the relevant sources aren’t readily available online. It’s a lot better with computer science than philosophy, for example. Output was frequently irrelevant or inaccurate for literary stuff.
But here’s the thing. Even if Perplexity Pro performed acceptably across the board, I wouldn’t pay $20 a month for it – let alone $200 for Max. I’ve got cats to feed. And there are plenty of tried-and-true ways to dig out articles and books, as well as other free LLM-based tools that could point me in the right (or wrong) direction.
The reality is that application-layer companies like Perplexity are likely taking losses on many of their users, whether that’s offering people like me premium subscriptions for free or not passing on the full cost of tokens to heavy users of flat-rate plans. Subscriber numbers get inflated, while profits are slim relative to operating costs.
It’s a similar story for the companies making the models these applications rely on. Until recently, OpenAI and Anthropic’s flat-rate plans mostly obscured the true inference costs associated with tasks. When Anthropic introduced metered billing for enterprise customers earlier this year, we saw companies place tighter controls on AI spend.
When used correctly, these tools can do useful stuff. But that usefulness doesn’t justify their actual cost (let alone their environmental cost). AI companies can’t keep subsidising their users’ tokens forever. And there’s limited evidence that either enterprises or consumers will be willing to pick up the bill.
So, the unit economics are questionable and these companies rely on unprecedented levels of funding to keep juicing their growth. What could possibly go wrong?
gabriel burrow 