Top Companies' AI Moves

Today's Stories

1. 1

   Chai partners with Bristol Myers Squibb on AI antibody discovery

   Chai, an AI platform for drug discovery, announced a collaboration with Bristol Myers Squibb to use AI for identifying antibodies that could treat disease. AI-driven antibody discovery could accelerate the timeline and reduce costs for developing new therapeutic candidates, an area where traditional methods are slow and expensive. For pharmaceutical companies, this represents a pathway to speed up their research pipelines.

   The specific scope, timeline, and financial terms of the partnership were not detailed in the announcement.

2. 2

   Hark and AT&T partner on connectivity for future AI devices

   Hark and AT&T have announced a partnership focused on providing connectivity solutions for future AI devices. As AI applications expand beyond traditional computing, reliable connectivity will be essential for these new devices to function. The partnership signals that major telecommunications providers are preparing infrastructure to support the next generation of AI-powered hardware.

   Details on which specific AI devices will be supported, the timeline for rollout, and pricing or availability terms have not yet been disclosed.

3. 3

   Deere raises 2026 profit forecast on AI construction surge

   John Deere raised its full-year 2026 profit guidance after reporting a quarterly earnings beat driven by strong construction equipment demand linked to AI infrastructure buildout. The upgrade signals that major equipment manufacturers are benefiting from the capital investment wave flowing into data centers and AI systems. For industrial and logistics companies, this suggests sustained demand for heavy machinery as long as AI infrastructure deployment continues.

   Deere's revised 2026 outlook and the pace of AI-driven construction orders in coming quarters, which will indicate whether this demand boost is temporary or structural.

4. 4

   AT&T cuts Anthropic spending with open-source AI models

   AT&T is deploying open-source AI models to reduce its reliance on Anthropic's commercial services and lower its spending on the AI company's products. Enterprise customers are increasingly exploring cost-saving alternatives to premium AI vendors, signaling that organizations can manage expenses by mixing commercial and freely available models — a shift that may pressure AI vendors to justify premium pricing.

   The specific models AT&T has chosen and the degree to which this strategy reduces Anthropic spending will indicate whether enterprises view open-source options as genuine substitutes for commercial AI services.

5. 5

   Zayo expands Corning fiber deal for AI data center buildout

   Zayo, a network infrastructure provider, has expanded its partnership with Corning, a materials science company, to secure a long-term supply of fiber optic cables for its AI-focused data center expansion. Fiber optic cables are critical infrastructure for data centers that power AI services. By locking in a long-term supply agreement with Corning, Zayo is securing a key input needed to scale its capacity as demand for AI infrastructure grows. This move suggests Zayo expects sustained demand for high-capacity network connectivity to support AI workloads.

   The details of the agreement—including contract duration, volume commitments, and pricing terms—will indicate how aggressively Zayo plans to expand its AI-ready data center footprint and whether this partnership model becomes standard as other infrastructure providers compete for AI-driven growth.

6. 6

   Pharma bets on AI to cut drug development costs, timelines

   The pharmaceutical industry is increasing investment in artificial intelligence to accelerate drug discovery and reduce development costs and timelines. Drug development traditionally requires years and billions of dollars; AI tools that can speed up early-stage research and candidate screening could substantially lower barriers to bringing new treatments to market, potentially benefiting both large pharma companies and smaller biotech firms seeking cost efficiencies.

   Implementation progress in clinical trials and real-world validation of AI-predicted drug candidates, as the sector moves from research pilots to commercialization.