Consumer advocates criticize Democratic Governor Jared Polis for siding with major corporations instead of protecting workers. Polis vetoed a bill that would have prohibited companies from using surveillance data to determine wages and consumer prices. This proposed legislation would have been the strictest in the U.S. addressing algorithm-driven pricing methods. Maryland recently enacted a law banning surveillance pricing in grocery stores, but Colorado’s bill aimed to apply more broadly across various sectors.
South Korea's Kospi stock index has surged to unprecedented highs, propelled by the AI-driven chip market. The nation has climbed to become the world's sixth-largest equity market, surpassing India and leaving European markets behind. Notably, chipmakers SK Hynix and Samsung Electronics, along with Taiwan's TSMC, have each surpassed the trillion-dollar valuation mark thanks to soaring demand for AI chips. However, experts caution about the potential volatility of this rapid growth and the heavy dependence on just two major semiconductor firms within South Korea's market.
Uber has limited its employees' AI spending following an initial push that encouraged extensive AI use among staff. This adjustment comes as the company realized it had quickly exceeded its allocated budget within just four months.
According to The Information, Salesforce's acquisition of the AI platform Contentful, originally founded in Germany, was valued between $1 billion and $1.5 billion. This strategic move strengthens Salesforce's position in the artificial intelligence space, enhancing its technology offerings and market reach.
Fonoa, based in Dublin, has successfully raised $110 million in a funding round led by Headline. New investors Eurazeo and Forestay Capital joined existing supporters Index Ventures, OMERS, Coatue, and Dawn Capital. The company also acquired a tax platform from PwC as part of its expansion strategy.
A recent survey by Bain & Co. involving 951 large companies highlights a significant gap between AI cost-saving expectations and actual outcomes, with executives finding results underwhelming.
In a market where products can be easily replicated, trust has emerged as the key factor that sets luxury brands apart from the rest.
Explore how adopting a firm no-negotiation stance on job offers can help create fairer pay structures and maintain equity. Learn effective strategies to communicate this policy persuasively to candidates, ensuring you attract and retain the best talent without losing out to competitors.
Perplexity AI, a rapidly growing search startup valued at $20 billion, showcased its groundbreaking hybrid local-cloud inference orchestrator at Computex 2026. This system dynamically directs AI workloads—some processed locally on devices using Intel's Core Ultra Series 3, others routed to advanced cloud models—balancing privacy, accuracy, and efficiency in real time. CEO Aravind Srinivas and Intel CEO Lip-Bu Tan demonstrated how sensitive data remains on the device while complex analysis proceeds in the cloud, eliminating the need for users to preselect task execution locations. This technology promises to reduce cloud costs, enhance latency, and meet strict data governance requirements, particularly for regulated enterprises such as finance and healthcare. Alongside industry moves by Nvidia and Intel unveiling new AI-optimized chips, Perplexity's approach could shift how AI workloads are managed, empowering more on-device intelligence and influencing global infrastructure strategies. Despite nine pending lawsuits over copyright issues, Perplexity advances enterprise offerings with numerous integrations and compliance features. The hybrid inference feature, set to launch soon, represents a pivotal evolution in AI orchestration, competing with tech giants deploying similar local-cloud architectures but lacking Perplexity's dynamic task routing capability.
In early 2026, VentureBeat's Pulse Research exposed the “Governance Mirage” highlighting a gap between AI governance structures and actual control layers in enterprises. Nearly half identified central AI governance ownership, but many reported confusion or obstacles like vendor opacity. The critical failure in AI deployments isn’t the intelligence of models but the runtime infrastructure. Enterprises relying on stateless setups like Python scripts and LangChain face challenges: lost context after restarts, escalating costs, and compounding errors leading to failures, causing teams to spend excessive time on maintenance rather than innovation.
The survey of 132 tech leaders across industries reveals that treating runtime durability as a core engineering priority separates successful deployments from failures reminiscent of past RPA pitfalls. Key findings include significant engineering effort spent on «plumbing» systems, a costly observability burden especially on Microsoft platforms, and a fragmented migration away from stateless architectures toward durable, multi-layered orchestration approaches. Firms seek a balance between AI autonomy and human oversight, with user acceptance rates becoming a critical metric for production readiness.
Security remains a challenge with no dominant strategy, as enterprises adopt varied approaches like policy-as-code and sandboxing to protect sensitive data. The shift toward architectures combining flexibility with durability—the “polyglot” approach—is emerging as a frontrunner, reflecting firms’ desire for control beyond vendor lock-in. The report underscores that while models are increasingly capable, the runtime environment and economic factors largely dictate AI’s enterprise viability today.
