Top Companies' AI Moves

Today's Stories

1. 1

   Job candidates felt unfairly judged by AI avatars matching them on just one trait

   Researchers from Technical University of Munich and Lund University studied how approximately 220 job interview participants from Germany, the United Kingdom, and the United States perceived fairness in AI hiring decisions. They interacted with photorealistic avatars (programmed as either male or female, with dark or light skin) that conducted simulated interviews for a customer support position. After all participants received rejection, eye-tracking and questionnaire data revealed that those who matched the avatar in only one characteristic—either gender or skin color—judged the decision most unfairly, rating it more negatively than participants who matched in both characteristics or differed completely. Companies increasingly use AI avatars to conduct job interviews and make hiring decisions, partly because AI is widely believed to be less biased than humans. However, this study shows that even when the underlying AI model itself is unbiased, applicants' perception of fairness depends on the avatar's appearance and how it aligns with their own. This suggests that social reactions to avatars can override trust in the technology itself, creating a fairness problem that purely technical bias-reduction cannot solve.

   The research team emphasizes that insights into social behavior must be given greater consideration in AI design if recruitment processes are to be perceived as fair by all involved. The findings are published in the Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems.

2. 2

   AI security cost squeeze: defenders outspend attackers, must change tactics

   Companies are rationing AI spending as costs exceed budgets—Uber burned through its annual AI budget in four months and capped employee access to agentic coding tools at $1,500 per month. In cybersecurity, the imbalance is acute: attackers need cheap, open-source AI to target one application, while defenders must continuously test thousands of applications across their entire portfolio, mostly on expensive frontier models. The economics of cyber defense have fundamentally shifted. An attacker runs a capable open-source model continuously until finding one entry point; a defender running frontier models continuously across an enterprise portfolio pays orders of magnitude more for the same method. Most defensive tasks do not require the world's most advanced AI model, yet organizations price them as if they do—making the defender's cost curve unsustainable.

   The winning approach will combine proprietary specialized models with frontier models, routing each task to the model that performs best for it, rather than relying on frontier models alone. Organizations must own the full offensive AI stack—models, prompts, and routing logic—and continuously benchmark and optimize the system for recall, precision, and cost.

3. 3

   Lowe's deploys AI and acquisitions to capture pro customers

   Lowe's Companies reported industry-leading revenue growth among major home improvement retailers and is deploying AI tools alongside targeted acquisitions to expand sales to professional customers such as contractors and tradespeople. The effort signals Lowe's intent to deepen relationships with the professional segment and sharpen its competitive position within the home improvement retail sector. For professional customers, this may mean more tailored service and solutions through AI-powered offerings.

   The stock trades at US$211.63, about 20% below the consensus analyst target of US$263.73. Investors should monitor how effectively the company executes on revenue growth and professional customer expansion through these initiatives, particularly given the company's high debt and negative shareholders' equity on its balance sheet.

4. 4

   Palo Alto CEO calls for 90% token cost drop to spur AI adoption

   Palo Alto Networks CEO Nikesh Arora told CNBC that token costs need to fall as much as 90% to promote large-scale AI adoption. He said token efficiency needs to drop to 20% over the next 12 months, and 90% by the following year, after OpenAI's latest model achieved 54% better token efficiency for agentic coding. High token costs have emerged as a major pain point for businesses and are creating barriers to widespread AI adoption. The current pricing makes AI tools increasingly difficult for enterprises to implement. Arora is among a growing group of executives, including Palantir's CEO, warning that token pricing is preventing many businesses from deploying AI tools at scale.

   The token problem is already driving many businesses toward cheaper open-weight models, including Chinese models that are closing the gap with American labs. Arora expects the market will rationalize over time as demand remains high and technology becomes more efficient, or businesses will adjust their budgets accordingly.