AI companies are pivoting from creating gods to building products

Determinism, Reliability, and Where AI Fits in the Stack

• Many argue generative models are too nondeterministic to serve as foundational components: you can’t reliably “stack” systems on top of outputs that are wrong 5–10% of the time.
• Others respond that traditional software is not perfect either, but critics counter that ordinary code failures are orders of magnitude rarer and more predictable.
• Suggested pattern: use AI for suggestions, summarization, and statistical tasks (e.g., sentiment over thousands of reviews), then hand off to deterministic systems for critical actions (payments, bookings).
• Some see opportunity in domains where verification is automatic (e.g., test generation that only counts compilable, runnable tests that improve coverage).

Productization vs. “We’re an AI Company” Hype

• Strong sentiment that companies are starting from “we have AI, now find a product,” similar to earlier “.com,” “mobile,” and “blockchain” bubbles.
• Several commenters argue AI should be treated like any other tool (like Python), not the core identity of a company.
• Others defend tech-first exploration: for large technological shifts, it can be rational to ask “what business can ride this wave?” even before specific user demand is clear.
• Consensus that the market will eventually separate substantial products from shallow “AI-washed” offerings.

Chatbots and User Experience

• Many dislike generic AI chatbots embedded into websites (e.g., car dealerships), seeing them as cost-cutting measures that worsen service, similar to forced self-checkout.
• Distinction drawn between:
  • Standalone assistants (like general-purpose LLM chat) that heavy users find highly valuable.
  • Context-specific chatbots on sites, which often feel clumsy, misaligned with user goals, and mistrusted.

Concrete Uses and Limitations of LLMs

• Praised uses: coding help, shell/SQL snippets, parameter lookups, rough calculations, translations, brainstorming, and as a more focused alternative to web search.
• Heavy users claim dramatic time savings; they’re comfortable spotting and correcting errors.
• Others emphasize frequent hallucinations and misleading confidence, especially in factual or medical contexts, and warn against trusting outputs without verification or expertise.
• Debate over “using it wrong”: some say you must learn how to prompt and verify; critics see that as evidence AI products are still immature for general users.

AI as Augmentation Inside Products

• Some builders describe starting with a fully autonomous “AI agent” vision, then pivoting to more traditional apps where AI automates sub-tasks and humans review or control key steps.
• Common emerging pattern: “normal product with AI under the hood,” rather than “AI replaces the entire interface or workflow.”