Creating trustworthy generative AI requires resources probably on the scale of what companies like Microsoft and Google possess, making them even more powerful
Christopher Mims / Wall Street Journal :
Context & Ripple Effects
Christopher Mims' argument lands in the middle of a tension the coverage has been building all year: the [[a:838808|success of ChatGPT pushed Microsoft and Google to take greater AI risks despite their own ethics guidelines]], and the [[a:837610|experts' caution that generative AI's short-term promise and perils are more modest than the fervor]] around ChatGPT suggests. His point connects the two — trust and safety work isn't a constraint the giants chafe against so much as a cost center only they can afford, which turns responsible-AI spending into a market-power moat.
First-order effects
- Enterprises deciding which generative AI to deploy face a trust premium that only Microsoft- and Google-scale balance sheets can underwrite, steering procurement toward the two incumbents named in the piece.
Second-order effects
- Smaller labs and startups are squeezed into partnering with, or licensing from, the hyperscalers to reach the same trust bar — while the giants' own safety commitments bend under the competitive pressure documented in the NYT's internal-documents reporting.
Third-order effects
- If trustworthiness is a function of scale, AI safety becomes a structural moat: the industry consolidates around a handful of players, and regulators face the awkward position of writing rules that only incumbents can afford to meet.
The trend: Trust and safety in generative AI is shifting from a shared obligation to a scale-dependent capability that concentrates market power in the largest cloud and platform companies.