Anthropic CEO Dario Amodei’s call to “pace the frontier” has found unusual support from two of his biggest rivals. OpenAI CEO Sam Altman and xAI founder Elon Musk have all backed a slower, more coordinated approach to developing the most powerful AI models.
But is this a genuine push for safety, a strategic move by companies already leading the AI race, or both?
In this CX Today interview, Dan Balistierri, Principal Consultant at CX Advisory, discusses what the frontier AI slowdown debate could mean for enterprises already deploying AI in customer experience.
The conversation moves beyond the safety arguments coming from Silicon Valley. It looks at AI competition, independent oversight, customer transparency, contact center security, and the governance work CX leaders need to do now.
What Does the AI Slowdown Debate Mean for CX?
Amodei’s proposal does not call for a blanket halt on AI development. In his essay, “We Must Pace the Frontier”, he argues that safety testing, alignment work, and external scrutiny must keep pace with advances in model capability.
In the interview, Dan Balistierri made the case for a balanced approach. Competition can bring valuable innovation, particularly as enterprises look for better ways to support customers and employees. However, AI development also needs practical oversight that helps prevent misuse without blocking responsible progress.
That balance matters in customer experience. Contact centers often manage sensitive personal and financial information, which makes them a potential target for bad actors. As AI becomes more capable, businesses need clear safeguards around authentication, data access, employee guidance, and incident response.
“Competition is always valuable.”
The key is not to slow every AI project because risks exist. It is to make sure organizations understand those risks, build the right controls, and deploy technology with accountability from the start.
What Does Frontier AI Safety Mean for CX?
The frontier AI debate can sound distant from contact center operations. In practice, its core concerns already shape customer experience. Organizations need to understand how AI uses customer data, when automation influences an interaction, and who owns the outcome when an AI-supported answer causes harm.