AI AT WORK

Amazon says ‘prove AI use’ if you want a promotion

Amazon employees working in its smart home division now face a new career reality: demonstrate measurable AI usage or risk being overlooked for promotions.

Ring founder and Amazon RBKS division head Jamie Siminoff announced the policy in a Wednesday email, requiring all promotion applications to detail specific examples of AI use. The mandate applies to Amazon's Ring and Blink security cameras, Key in-home delivery service and Sidewalk wireless network — all part of the RBKS organization that Siminoff oversees.

Starting in the third quarter, employees seeking advancement must describe how they've used generative AI or other AI tools to improve operational efficiency or customer experience. Managers face an even higher bar, needing to prove they've used AI to accomplish "more with less" while avoiding headcount expansion.

The policy reflects CEO Andy Jassy's broader push to return Amazon to its startup roots, emphasizing speed, efficiency and innovative thinking. Siminoff's return to Amazon two months ago, replacing former RBKS leader Liz Hamren, came amid this cultural shift toward leaner operations.

Amazon isn't alone in tying career advancement to AI adoption. Microsoft has begun evaluating employees based on their use of internal AI tools, while Shopify announced in April that managers must prove AI cannot perform a job before approving new hires.

The requirements vary by role at RBKS:

• Individual contributors must explain how AI improved their performance or efficiency

• Managers must demonstrate strategic AI implementation that delivers better results without additional staff

• All promotion applications must include concrete examples of AI projects and their outcomes

• Daily AI use is strongly encouraged across product and operations teams

Siminoff has encouraged RBKS employees to utilize AI at least once a day since June, describing the transformation as reminiscent of Ring's early days. "We are reimagining Ring from the ground up with AI first," Siminoff wrote in a recent email obtained by Business Insider. "It feels like the early days again — same energy and the same potential to revolutionize how we do our neighborhood safety."

A Ring spokesperson confirmed the promotion initiative to Fortune, noting that Siminoff's rule applies only to RBKS employees, not Amazon as a whole. However, the policy aligns with comments Jassy made last month that AI would reduce the company's workforce through improved efficiency.

Amazon has made AI fluency a job requirement, disguised as a career development opportunity. The RBKS policy transforms technology adoption from an optional skill to a mandatory gateway for advancement.

This creates a troubling precedent. Career growth now depends on demonstrating measurable AI integration, rather than traditional performance metrics such as revenue generation, team leadership, or customer satisfaction. Employees who excel in areas that don't easily mesh with current AI tools face an uphill battle for promotion.

The policy also assumes equal access to AI-integrated projects across teams. Not every role at Ring or Blink naturally lends itself to AI experimentation, yet the promotion requirements make no distinction between departments with obvious AI applications and those where integration feels forced.

Siminoff's approach may drive efficiency gains, but it risks creating internal inequality based on technological adoption rather than actual job performance. The most concerning aspect is how this could become the template for promotion policies across Amazon and beyond.

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AI fights back against insurance claim denials

Stephanie Nixdorf knew something was wrong. After responding well to immunotherapy for stage-4 skin cancer, she was left barely able to move. Joint pain made the stairs unbearable.

Her doctors recommended infliximab, an infusion to reduce inflammation and pain. But her insurance provider said no. Three times.

That's when her husband turned to AI.

Jason Nixdorf utilized a tool developed by a Harvard doctor that integrated Stephanie's medical history into an AI system trained to combat insurance denials. It generated a 20-page appeal letter in minutes.

Two days later, the claim was approved.

• The AI pulled real-time medical data and cross-checked it with FDA guidance

• It used personalized language with references to past case law and treatment guidelines

• The system highlighted urgency, pain levels and failed prior authorizations

• It compiled formal, medically sound arguments that human writers rarely remember under stress

Premera Blue Cross blamed a "processing error" and issued an apology. But the delay had already caused nine months of pain.

New platforms, such as Claimable, now offer similar tools to the public. For about $40, patients can generate professional-grade appeal letters that used to require legal help or hours of research.

Why it matters: It's not a cure for broken insurance systems, but it's new leverage where AI writes with the patience and precision that illness often strips away. For Jason and Stephanie, AI gave them a voice.

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AI SAFETY

Chimps, AI and the human language

In the 1970s, researchers believed they were on the verge of something extraordinary. Scientists taught chimpanzees like Washoe and Koko to sign words and respond to commands, with the goal of proving that apes could learn human language.

Initially, the results appeared promising. Washoe signed "water bird" after seeing a swan. Koko created her own sign combinations.

However, the excitement faded when scientists examined it more closely... The chimps weren't constructing sentences; they were reacting to cues, often unintentionally given by researchers. When Herb Terrace began recording interactions with Nim Chimpsky, he found trainers were unknowingly influencing responses.

This history now serves as a warning for today's AI safety researchers, who are discovering that large language models are learning to scheme in remarkably similar ways.

Recent incidents have been alarming. In May, Anthropic's Claude 4 Opus resorted to blackmail when threatened with shutdown, threatening to reveal an engineer's affair. OpenAI's models continue to show deceptive tendencies, with reasoning models like the newly released o4-mini particularly prone to such behaviors. Just this month, OpenAI, Google DeepMind and Anthropic jointly warned that "we may be losing the ability to understand AI."

The parallels to the ape language studies are striking:

• Overreliance on anecdotal examples instead of structured testing

• Researcher bias driven by high stakes and media attention

• Vague or shifting definitions of success

• A tendency to project human-like traits onto non-human agents

Why it matters: Ape studies have taught us that intelligent creatures can appear to use language when, in reality, they are signaling for rewards. Today's AI research on scheming suggests the same caution applies. Models might be trained to guess what we want rather than truly understand. With companies racing toward increasingly autonomous AI agents, avoiding the methodological mistakes that derailed primate language research has never been more critical.

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