AI models can provide answers, but they lack innate curiosity. The unique and enduring value of humans, especially in fields like journalism, is their ability to ask insightful questions. This positions human curiosity as the essential driver for AI, rather than a skill that AI will replace.
The AI fundraising environment is fueled by investors' personal use of the products. Unlike B2B SaaS where VCs rely on customer interviews, they directly experience the value of tools like Perplexity. This firsthand intuition creates strong conviction, contributing to a highly competitive investment landscape.
Perplexity achieves profitability on its paid subscribers, countering the narrative of unsustainable AI compute costs. Critically, the cost of servicing free users is categorized as a research and development expense, as their queries are used to train and improve the system. This accounting strategy presents a clearer path to sustainable unit economics for AI services.
Perplexity's talent strategy bypasses the hyper-competitive market for AI researchers who build foundational models. Instead, it focuses on recruiting "AI application engineers" who excel at implementing existing models. This approach allows startups to build valuable products without engaging in the exorbitant salary wars for pre-training specialists.
While tech giants could technically replicate Perplexity, their core business models—advertising for Google, e-commerce for Amazon—create a fundamental conflict of interest. An independent player can align purely with the user's best interests, creating a strategic opening that incumbents are structurally unable to fill without cannibalizing their primary revenue streams.
The company actively works to prevent its answer engine from being gamed by "AI SEO" tactics. The core purpose is to maintain accuracy and trustworthiness; if a user can manipulate the results, that trust is broken. Perplexity views it as an arms race, stating they have "better engineers" to patch any hacks that so-called AI SEO firms might discover.
The primary impact of AI in investment banking isn't headcount reduction but a massive productivity lift. By automating 80% of the work for initial drafts of pitch decks and models, AI frees up senior bankers' bandwidth. This allows them to pursue a greater number of new engagements, fundamentally expanding the firm's capacity for new business.