Amazon CEO on AI and workforce
Amazon CEO Andy Jassy made headlines with a statement about AI’s impact on the corporate workforce. As the company rolls out more AI agents, he said, some jobs will be eliminated while others are created. In the next few years, the company expects this will reduce total corporate workforce through efficiency gains.
The hosts noted this may signal a fundamental break between growth and hiring. Historically, more production meant more people. It could now mean more AI agents instead.
Who would have thought the safer job at Amazon might be the delivery driver versus the AI software engineer who is going to be displaced by AI agents?
Andy Hoar, Master B2B
Options versus answers demonstrated
The hosts showed a side-by-side comparison of Google versus ChatGPT for the query best cordless screwdriver under $100. Google displays sponsored products, comparison articles, and listings. ChatGPT provides specific recommendations with pros, cons, use case guidance, and a statement that it selects products independently.
The difference is options versus answers. Google helps users evaluate choices. ChatGPT makes the choice and explains its reasoning. This shift changes what companies must optimize for.
EAT drives AI selection
The hosts explained that expertise, authoritativeness, and trustworthiness (EAT) determine how AI models select sources. AI systems make judgments about credibility based on semantic understanding rather than keyword matching. Content without demonstrated authority gets ignored by AI systems. Misinformation gets penalized.
It is hard to game it. There will be ways, but it is a much steeper hill to climb. You cannot just blanket the site with keywords and pay your way into it.
Andy Hoar, Master B2B
Authoritative sources matter
The hosts identified sources that AI models consider authoritative: academic institutions, government agencies, established news sources, trade associations, trade publications, peer-reviewed journals, and statistical sources like Pew Research or the Census Bureau. Being cited by these sources increases the likelihood of appearing in AI responses.
Notably absent from the list: ratings sites. The hosts observed that rating credibility has been undermined by fake reviews, and AI models may weight these sources less than sources with demonstrated editorial standards.
Amazon’s Rufus dilemma
The hosts questioned whether Amazon’s Rufus AI assistant will follow the Google model of sponsored content or the ChatGPT model of objective recommendations. Amazon faced credibility issues with product reviews when fake ratings became common. If Rufus recommendations appear influenced by advertising, customers may lose trust.
The hosts noted Amazon can choose objectivity more easily than Google because its revenue is not entirely dependent on advertising. AWS can subsidize the commerce business in ways Google cannot subsidize its search business.
Use case reviews gain importance
The hosts predicted that detailed use-case reviews will become more valuable for AI recommendations. Reviews describing specific applications, comparing alternatives, and explaining real-world outcomes provide objective information AI models value. Generic five-star reviews add little. Substantive reviews describing how products were used and what worked carry weight.
This represents an opportunity for B2B companies to encourage customers to share detailed use case documentation rather than simple satisfaction ratings.
Building authority over time
The hosts concluded with practical guidance: showcase real-world expertise through original content, cite credentials and authority, build digital footprint through third-party mentions, maintain structured high-quality content, demonstrate trustworthiness through transparency, leverage LinkedIn thought leadership, and get cited by trusted sources. Authority must be built over time through consistent presence, not purchased through advertising.