Will AI be the basis of many future industrial fortunes, or a net loser?

Where AI Wealth Accrues: Platforms vs “Little Guys”

• One side argues major model providers and cloud platforms will capture most value: they control access, can raise prices, cut off successful customers, and deeply integrate AI into dominant ecosystems (Office, iOS, etc.).
• Others counter with historical examples (PCs, web, smartphones) where small entrants, not incumbents, built the breakout products; they expect new AI-native ideas and “little guys” to find undiscovered opportunities.
• Several note OpenAI itself may be squeezed between Big Tech and state-backed labs; hardware (chips, energy, fabs) and infra vendors (NVIDIA, cloud) look like clearer winners.

AI as Cost Reducer and Barrier-Lowering Tool

• Many comments describe AI as “GarageBand/iMovie for everything”: great for hobbyists, indie game devs, solo founders to produce “good enough” assets, prototypes, copy, and code.
• Lower barriers mean more entrants, more competition, and harder differentiation; easier to start, harder to stand out or make money.
• Some fear AI simply lets customers do for $20/month what they previously paid specialists or startups for, potentially shrinking entire service markets.

Democratization vs Commoditization and Monopolies

• One camp sees broad consumer surplus: individuals capture most benefit, while AI providers become low-margin utilities, similar to shipping containers or factory automation.
• Others warn of concentration: once content and apps are trivial to produce, distribution and attention monopolies (search, social, app stores) become even more powerful.

Impact on Work, Skills, and Creativity

• Expected big productivity gains in non-physical work (coding, requirements, marketing, design), but with unpredictable job displacement and erosion of entry-level learning paths.
• Strong disagreements over AI art/music/text: some see it as empowering self-expression and prototyping; others call it derivative “slop,” harmful to human artists, and built on unconsented training data.

Technical Limits, Hype, and AGI Speculation

• Heated subthread on whether AI can ever solve inherently chaotic problems like long-range weather forecasting; one side cites chaos theory limits, the other insists future models and compute will push horizons out.
• LLMs are described both as dangerous “BS generators” when treated as fact sources, and extremely useful when treated as pattern-completion tools embedded in workflows.
• Views on the future range from “another overhyped bubble like crypto” to “early phase of something as transformative as microprocessors or smartphones,” with little agreement on predictability.