The 'use AI for safety' plan adopted by frontier labs is most likely to fail not because alignment techniques are ineffective, but because competitive pressures will prevent them from redirecting a meaningful fraction of their AI labor away from capabilities research and towards safety work when it matters most.

Contrary to popular belief, the most pressing talent gaps in impactful AI organizations are not solely technical. There is a huge demand for experienced professionals in management, HR, communications, and operations to help these organizations scale effectively.

AI safety organizations struggle to hire despite funding because their bar is exceptionally high. They need candidates who can quickly become research leads or managers, not just possess technical skills. This creates a bottleneck where many interested applicants with moderate experience can't make the cut.

The primary constraint for AI safety organizations like Meter is a lack of technical talent, not access to frontier models. They are in a "state of triage," turning down research opportunities because they lack the staff to pursue critical safety questions, a key vulnerability in the ecosystem.

AI leaders aren't ignoring risks because they're malicious, but because they are trapped in a high-stakes competitive race. This "code red" environment incentivizes patching safety issues case-by-case rather than fundamentally re-architecting AI systems to be safe by construction.

While AI alignment gets attention (~1,000 researchers), the risk of a single entity (a company or state) using AI for irreversible power concentration has far fewer people working on it (~20). This makes it one of the most highly-leveraged areas for impact.

There's a significant disconnect between interest in AI safety and available roles. Applications to programs like MATS are growing over 1.5x annually, and intro courses see 370% yearly growth, while the field itself grows at a much slower 25% per year, creating an increasingly competitive entry funnel.

A key failure mode for using AI to solve AI safety is an 'unlucky' development path where models become superhuman at accelerating AI R&D before becoming proficient at safety research or other defensive tasks. This could create a period where we know an intelligence explosion is imminent but are powerless to use the precursor AIs to prepare for it.

While thousands work on AI safety, the field is severely neglected relative to the problem's potential scale. For perspective, the Nature Conservancy alone employs more people (3,000-4,000) than the estimated number working globally on the most severe risks from AGI, highlighting a massive resource disparity.